UPSI Digital Repository (UDRep)
|
![]() |
|
|
Abstract : Perpustakaan Tuanku Bainun |
Internet of Vehicles (IoV) technology has been rapidly advancing, making intelligent transportation systems the future trend. This research revolves around building efficient and secure vehicular networks using data processing mechanisms backed by machine learning and information security. However, noise and incremental data present challenges to vehicular network development. This study proposes two novel federated learning frameworks, namely the Outlier Detection and Exponential Smoothing Federated Learning (OES-FED) and Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (FED-IW&DS), to overcome the above problems. The OES-FED framework leveraged anomaly detection and exponential smoothing to filter noise data, thus, improving model robustness and enhancing communication efficiency. In terms of accuracy, it outperformed the existing Federated Learning-Average (FED-AVG) and FED-SGD models on three datasets by 44.46% and 2.36%, respectively. Furthermore, the FED-IW&DS framework that integrates incremental weights and diversity selection to effectively deal with issues of growing data scale was able to achieve rapid information sharing while preserving user privacy. The superiority of FED-IW&DS was clearly proven through its performance on two data sets, which found its accuracy to exceed that of the Fed-prox model by 30- 35%. Ultimately, integrating the OES-FED and FED-IW&DS frameworks unveiled two critical integration points: the execution order and transition point of the two frameworks. By synergistically integrating the two frameworks, the proposed strategy unlocked new federated learning solutions for IoV as it yielded up to 5-10% higher accuracy compared to employing either framework individually. This study highlights novel approaches that address noise and incremental data challenges in IoV, yielding substantial advancements in both theoretical research and practical applications. The research outcomes have several implications, of which the proposed solutions play an essential role in improving communication efficiency, enhancing data processing capabilities, protecting user privacy, and providing crucial theoretical support and practical reference for future research and optimization of data processing mechanisms in IoV. |
References |
Acheampong, R. A., Cugurullo, F., Gueriau, M., & Dusparic, I. (2021). Can autonomous vehicles enable sustainable mobility in future cities? Insights and policy challenges from user preferences over different urban transport options. Cities, 112,103134. https://doi.org/10.1016/j.cities.2021.103134 Ahmad,K.,&Bale,T.A. (2001).Simulationof quantificationabilitiesusingamodular neural network approach. Neural Computing & Applications, 10(1), 77–88. https://doi.org/10.1007/s521-001-8046-1 Ahmad, W., Rasool,A., Javed,A. R., Baker, T., &Jalil, Z. (2022).Cyber securityin IoT-based cloud computing: A comprehensive survey. Electronics, 11(1), 16. https://doi.org/10.3390/electronics11010016 Ahmed, L., Ahmad, K., Said, N., Qolomany, B., Qadir, J., & Al-Fuqaha, A. (2020). Active learning based federated learning for waste and natural disaster image classification. IEEE Access, 8,208518–208531. Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020). Federated learning: A survey on enabling technologies, protocols, and applications. IEEE Access, 8, 140699–140725. https://doi.org/10.1109/ACCESS.2020.3013541 Alotaibi,J.,&Alazzawi, L. (2022). PPIoV:Aprivacypreserving-based frameworkfor IoV-Fogenvironmentusingfederatedlearningandblockchain.InR.Paul(Ed.), 2022 IEEE World AI IOT Congress (pp. 597–603). IEEE. https://doi.org/10.1109/AIIoT54504.2022.9817205 Alqahtani,A. S.,Trabelsi,Y.,Ezhilarasi,P.,Krishnamoorthy,R.,Lakshmisridevi,S.,& Shargunam, S. (2024). Homomorphic encryption algorithm providing security and privacy for IoT with optical fiber communication. Optical and Quantum Electronics, 56(3),487. https://doi.org/10.1007/s11082-023-06098-5 Al-Sharman, M., Murdoch, D., Cao, D., Lv, C., Zweiri, Y., Rayside, D., & Melek, W. (2021).Asensorlessstateestimationfor asafety-orientedcyber-physicalsystem in urban driving: Deep learning approach. IEEE-Caa Journal of Automatica Sinica, 8(1),169–178. https://doi.org/10.1109/JAS.2020.1003474 Ambroziak,L.,Kownacki,C.,&Simha,A.(2022).Switchedcontrolstrategyfor robust formation flight with HIL and In-Flight validation. IEEE International Conference on Communications (Icc 2022). https://doi.org/10.1109/ICC45855.2022.9838250 Anbalagan, S., Raja, G., Gurumoorthy, S., Suresh, R. D., & Dev, K. (2023). IIDS: Intelligent intrusion detection system for sustainable development in autonomousvehicles.IEEE Transactions on Intelligent Transportation Systems, 24(12),15866–15875. https://doi.org/10.1109/TITS.2023.3271768 Anowar, F., & Sadaoui, S. (2020). Incremental neural-network learning for big fraud data. 2020 IEEE International Conference on Systems, Man, and Cybernetics, 3551–3557. https://www.webofscience.com/wos/woscc/summary/6d5cfd17f044-4879-ad54-691c2cc432ad-54465123/relevance/1 Ardabili, S., Mosavi, A., & Varkonyi-Koczy, A. R. (2020). Advances in machine learning modeling reviewing hybrid and ensemble methods. In A. R. VarkonyiKoczy (Ed.), Engineering for Sustainable Future (Vol. 101, pp. 215– 227). Springer International PublishingAg. https://doi.org/10.1007/978-3-03036841-8_21 Arias-Otalora, D.-S., Florez,A., Mellizo, G., Rodriguez-Garavito, C. H., Romero, E., &Tumialan,J.A.(2022).Amachinelearningbasedcommandvoicerecognition interface. In J. C. Figueroa-Garcia, C. Franco, Y. Diaz-Gutierrez, & G. Hernandez-Perez(Eds.), Applied Computer Sciences in Engineering, Wea 2022 (Vol. 1685, pp. 450–460). Springer International Publishing Ag. https://doi.org/10.1007/978-3-031-20611-5_37 Ayub,A.,&Wagner,A.R.(2020).Cognitively-inspiredmodelfor incrementallearning using a few examples. 2020 IEEE/Cvf Conference on Computer Vision and Pattern Recognition Workshops, 897–906. https://doi.org/10.1109/CVPRW50498.2020.00119 Babaghayou,M.,Labraoui,N.,Ferrag,M.A.,&Maglaras,L.(2021).Between location protectionandoverthrowing:Acontrarinessframeworkstudyfor smartvehicles. 2021 IEEE International Conference on Consumer Electronics. https://doi.org/10.1109/ICCE50685.2021.9427612 Baker-Eveleth,L., Stone, R., & Eveleth,D. (2022).Understandingsocialmediausers’ privacy-protection behaviors. Information and Computer Security, 30(3), 324– 345. https://doi.org/10.1108/ICS-07-2021-0099 Balakrishnan,R.,Akdeniz,M.,Dhakal,S.,Anand,A.,Zeira,A.,&Himayat,N.(2021). Resource management and model personalization for federated learning over wireless edge networks. Journal of Sensor and Actuator Networks, 10(1), 17. https://doi.org/10.3390/jsan10010017 Banabilah, S.,Aloqaily, M.,Alsayed, E., Malik, N., & Jararweh,Y. (2022). Federated learning review: Fundamentals, enabling technologies, and futureapplications. Information Processing & Management, 59(6), 103061. https://doi.org/10.1016/j.ipm.2022.103061 Bao,W.,Wu,C.,Guleng,S.,Zhang,J.,Yau,K.-L.A.,&Ji,Y.(2021).Edgecomputingbased joint client selection and networking scheme for federated learning in vehicular IoT. China Communications, 18(6),39–52. Belschner, R., & Pereira, C. (1995). Mapping pearls high-level real-time constructs to a C run-time library under real-time unix. Control Engineering Practice, 3(6), 849–854. https://doi.org/10.1016/0967-0661(95)00069-7 Bhatia,R.,&Singh,N. P.(2022).Gender recognitionbyvoiceusingmachinelearning. InI.Woungang,S.K.Dhurandher,K.K.Pattanaik,A.Verma,&P.Verma(Eds.), Advanced Network Technologies and Intelligent Computing (Vol. 1534, pp. 307–318).Springer InternationalPublishingAg. https://doi.org/10.1007/978-3030-96040-7_25 Bhattacharyya, S., Neagu, A. T., & Firu, E. (2021). An investigation of bin-bin correlation by the method of factorial correlator in high-energy heavy ion collisions. International Journal of Modern Physics E, 30(12), 2150103. https://doi.org/10.1142/S0218301321501032 Birzu,C.,French,P.,Caccese,M.,Cerretti,G.,Idbaih,A.,Zagonel,V.,&Lombardi,G. (2021). Recurrent glioblastoma: From molecular landscape to new treatment perspectives. Cancers, 13(1),47. https://doi.org/10.3390/cancers13010047 Bolón-Canedo,V.,Remeseiro,B.,&Cancela,B.(2018).Featureselectionforbigvisual data: Overview and challenges. International Conference Image Analysis and Recognition,136–143. Bomfim, R.A. (2023). Last dental visit and severity of tooth loss:Amachine learning approach. BMC Research Notes, 16(1), 347. https://doi.org/10.1186/s13104023-06632-4 Breivold,H.P.,&Rizvanovic,L.(2018).Business modelinganddesignintheinternetof-things context. Proceedings 2018 IEEE 11th International Conference on Cloud Computing,524–531. https://doi.org/10.1109/CLOUD.2018.00073 Cao, B., Fan, S., Zhao, J., Tian, S., Zheng, Z.,Yan,Y., &Yang, P. (2021). Large-scale many-objective deployment optimization of edge servers. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3841–3849. https://doi.org/10.1109/TITS.2021.3059455 Cao, M., Zheng, L., Jia, W., & Liu, X. (2021). Joint 3D reconstruction and object tracking for traffic video analysis under IoV environment. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3577–3591. https://doi.org/10.1109/TITS.2020.2995768 Ceballos,G.R.,&Larios,V.M. (2016).Amodeltopromotecitizendrivengovernment in a smart city use case at GDL smart city. IEEE Second International Smart Cities Conference, 781–786. https://www.webofscience.com/wos/woscc/summary/ff312112-492c-4e5787ed-e610102a9875-60cc519f/relevance/1 Chamikara, M. a. P., Bertok, P., Khalil, I., Liu, D., & Camtepe, S. (2021). Privacy preserving distributed machine learning with federated learning. Computer Communications, 171, 112–125. https://doi.org/10.1016/j.comcom.2021.02.014 Chang, C.-C., & Li, C.-T. (2019). Algebraic secret sharing using privacy homomorphisms for IoT-based healthcare systems. Mathematical Biosciences and Engineering, 16(5),3367–3381. https://doi.org/10.3934/mbe.2019168 Chen, C. L. (1999). On double-byte error-correcting codes. IEEE Transactions On Information Theory, 45(6),2207–2208. https://doi.org/10.1109/18.782175 Chen, C., Liu, L., Wan, S., Hui, X., & Pei, Q. (2022). Data dissemination for industry 4.0 applications in internet of vehicles based on short-term traffic prediction. ACM Transactions on Internet Technology, 22(1), 3. https://doi.org/10.1145/3430505 Chen, H., & Li, J. (2019). Finding stable clustering for noisy data via structure-aware representation.InC.Baru,J.Huan,L.Khan,X.H.Hu,R.Ak,Y.Tian,R.Barga, C. Zaniolo, K. Lee, &Y. F. Ye (Eds.), 2019 IEEE International Conference on Big Data (pp. 46–55). IEEE. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000554828700010 Chen,L.,&Masayuki,M.(2020).Mitigatecatastrophicforgettingbyvaryinggoals.In A. P. Rocha, L. Steels, & J. VanDenHerik (Eds.), Proceedings of the 12th International Conference on Agents and Artificial Intelligence, Vol 2 (pp. 530– 537).Scitepress. https://doi.org/10.5220/0008950005300537 Chen, M., Wu, J.,Yin,Y., Huang, Z., Liu, Q., & Chen, E. (2022). Dynamic clustering federatedlearningfor non-IIDdata.InL. Fang,D. Povey,G. Zhai,T. Mei,&R. Wang (Eds.), Artificial Intelligence, CICAI 2022, PT III (Vol. 13606, pp. 119– 131). Springer International PublishingAg. https://doi.org/10.1007/978-3-03120503-3_10 Chen, M.,Yang, Z., Saad, W., Yin, C., Poor, H. V., & Cui, S. (2021).A joint learning andcommunicationsframeworkfor federatedlearningover wirelessNetworks. IEEE Transactions on Wireless Communications, 20(1), 269–283. https://doi.org/10.1109/TWC.2020.3024629 Chen,Y., Sun, X., & Jin,Y. (2020). Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation. IEEE Transactions on Neural Networks and Learning Systems, 31(10),4229–4238. https://doi.org/10.1109/TNNLS.2019.2953131 Chen, Z., Li, D., Zhao, M., Zhang, S., & Nu, J. (2020). Semi-federated learning. 2020 IEEE Wireless Communications and Networking Conference. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000569342900003 Cheng, J., Liu, Z., Shi, Y., Luo, P., & Sheng, V. S. (2023). GrCol-PPFL: User-based group collaborative federated learning privacy protection framework. CMC-Computers Materials & Continua, 74(1), 1923–1939. https://doi.org/10.32604/cmc.2023.032758 Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1).https://doi.org/10.1186/s12864-019-6413-7 Chicco, D., Totsch, N., & Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. Biodata Mining, 14(1), 13. https://doi.org/10.1186/s13040-021-00244-z Chitty-Venkata,K.T.,&Somani,A.K.(2021).Array-Awareneuralarchitecturesearch. 2021 IEEE 32nd International Conference on Application-Specific Systems, Architectures and Processors, 125–132. https://doi.org/10.1109/ASAP52443.2021.00026 Choi,J.,&Pokhrel,S. R.(2020).FederatedlearningwithmultichannelALOHA.IEEE Wireless Communications Letters, 9(4), 499–502. https://doi.org/10.1109/LWC.2019.2960243 Costa-Filho, C. F. F., Negreiro, J., & Costa, M. G. F. (2022). Multimodal biometric systembasedonautoencodersandlearningvector quantization. InT.F.Bastos-Filho, E. M. D. Caldeira, &A. Frizera-Neto (Eds.), XXVII Brazilian Congress on Biomedical Engineering, CBEB 2020 (pp. 1611–1617). Springer. https://doi.org/10.1007/978-3-030-70601-2_236 Cui,W.,Xia,W.,Lan,Z.,Qian,C.,Yan,F.,&Shen,L.(2018).Aself-adaptivefeedback handoff algorithm based decision tree for internet of vehicles. International Conference on Ad Hoc Networks,177–190. Cui,W.,Xia,W.,Lan,Z.,Qian,C.,Yan,F.,&Shen,L.(2019).Aself-adaptivefeedback handoff algorithm based decision tree for internet of vehicles. In J. Zheng,W. Xiang, P. Lorenz, S. Mao, & F. Yan (Eds.), Ad Hoc Networks, Adhocnets 2018 (Vol. 258, pp. 177–190). Springer International Publishing Ag. https://doi.org/10.1007/978-3-030-05888-3_17 Cumpston, M. S., McKenzie, J. E., Welch, V. A., & Brennan, S. E. (2022). Strengthening systematic reviews in public health: Guidance in the Cochrane handbookfor systematicreviewsof interventions,2ndedition.Journal of Public Health, 44(4),E588–E592. https://doi.org/10.1093/pubmed/fdac036 Curik,P., Ploszek,R.,& Zajac,P. (2022). Practical useof secretsharing for enhancing privacy in clouds. Electronics, 11(17), 2758. https://doi.org/10.3390/electronics11172758 Cuzzocrea, A. (2021). Big data lakes: Models, Frameworks, and techniques. In H. Unger, J. Kim, U. Kang, C. SoIn, J. Du, W. Saad, Y. G. Ha, C. Wagner, J. Bourgeois, C. Sathitwiriyawong, H. Y. Kwon, & C. Leung (Eds.), 2021 IEEE International Conference on Big Data and Smart Computing (pp. 1–4). IEEE. https://doi.org/10.1109/BigComp51126.2021.00010 Dai, X., Spasic, I., Chapman, S., & Meyer, B. (2020). The State of the art in implementing machine learning for mobile apps:Asurvey. IEEE Southeastcon 2020. Annual IEEE SoutheastCon Conference. https://doi.org/10.1109/southeastcon44009.2020.9249652 Dan, S., Bao, H., & Sugiyama, M. (2021). Learning from noisy similar anddissimilar data. In N. Oliver, F. PerezCruz, S. Kramer, J. Read, & J. A. Lozano (Eds.), Machine Learning and Knowledge Discovery in Databases, Ecml Pkdd 2021: Research Track, Pt Ii (Vol. 12976, pp. 233–249). Springer International PublishingAg. https://doi.org/10.1007/978-3-030-86520-7_15 Das, S. (2019). A machine learning model for detecting respiratory problems using voicerecognition. 2019 IEEE 5th International Conference for Convergence in Technology (I2ct). https://www.webofscience.com/wos/woscc/summary/30095666-097f-4532b5b4-1d7f8d0ca707-7c980e29/relevance/1 Deb, R., & Liew, A. W.-C. (2019). Noisy values detection and correction of traffic accident data. Information Sciences, 476, 132–146. https://doi.org/10.1016/j.ins.2018.10.002 Demircioglu, E. D., & Kalipsiz, O. (2022). API message-driven regression testing framework. Electronics, 11(17), 2671. https://doi.org/10.3390/electronics11172671 Dhada, M., Jain, A. K., & Parlikad,A. K. (2020). Empirical convergence analysis of federated averaging for failure prognosis. IFAC Papersonline, 53(3), 360–365. https://doi.org/10.1016/j.ifacol.2020.11.058 Dieci, M. V., Miglietta, F., & Guarneri, V. (2021). Immune infiltrates in breast cancer: Recent updates and clinical implications. Cells, 10(2), 223. https://doi.org/10.3390/cells10020223 Diu, J., Chen, Y., Tian, Z., Guizani, N., & Du, X. (2021). The security of internet of vehicles network: Adversarial examples for trajectory mode detection. IEEE Network, 35(5),279–283. https://doi.org/10.1109/MNET.121.2000435 Dlugosch,O.,Brandt,T., &Neumann,D. (2022).Combining analyticsand simulation methods to assess the impact of shared, autonomous electric vehicles on sustainable urban mobility. Information & Management, 59(5), 103285. https://doi.org/10.1016/j.im.2020.103285 Dong, Y., Yang, X., Li, J., Liao, G., Tian, K., & Guan, H. (2012). High performance network virtualization with SR-IOV. Journal of Parallel and Distributed Computing, 72(11),1471–1480. https://doi.org/10.1016/j.jpdc.2012.01.020 Du, B., & Wu, C. (2022). Federated graph learning withperiodic neighbour sampling. 2022 IEEE/ACM 30TH International Symposium On Quality Of Service (IWQOS). https://doi.org/10.1109/IWQoS54832.2022.9812908 Du,Z.,Wu,C.,Yoshinaga,T.,Yau,K.-L.A., Ji,Y.,& Li,J. (2020).Federatedlearning for vehicular internet of things: Recent advances and open issues. IEEE Open Journal of the Computer Society, 1,45–61. Duan, W., Gu, J., Wen, M., Zhang, G., Ji, Y., & Mumtaz, S. (2020). Emerging technologies for 5G-IoV networks: Applications, trends and opportunities. IEEE Network, 34(5),283–289. https://doi.org/10.1109/MNET.001.1900659 Duncan,K.,Komendantskaya,E.,Stewart,R.,&Lones,M.(2020).Relative robustness of quantized neural networks against adversarial attacks. 2020 International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn48605.2020.9207596 El Mazouzi, H., Khannous, A., Amechnoue, K., & Rghioui, A. (2023). Security challenges facing blockchain based-IoV network: A systematic review. International Journal of Advanced Computer Science and Applications, 14(5), 249–256. Elmidaoui, S., Cheikhi, L., Idri,A., &Abran,A. (2020). Machine learning techniques for softwaremaintainabilityprediction:Accuracyanalysis.Journal of Computer Science and Technology, 35(5), 1147–1174. https://doi.org/10.1007/s11390020-9668-1 Epstein, L. (2021). On bin packing with clustering and bin packing with delays. Discrete Optimization, 41, 100647. https://doi.org/10.1016/j.disopt.2021.100647 Fachola,C.,Tornaria,A.,Bermolen,P.,Capdehourat,G.,Etcheverry,L.,&Fariello,M. I. (2023). Federated learning for data analytics in education. Data, 8(2), 43. https://doi.org/10.3390/data8020043 Fan, H., & Zhou, Z. (2023). Privacy-preserving data aggregation scheme based on federated learning for IIoT. Mathematics, 11(1), 214. https://doi.org/10.3390/math11010214 Fang, C., Guo,Y., Wang, N., & Ju,A. (2020). Highly efficient federated learning with strong privacy preservation in cloud computing. Computers & Security, 96, 101889. https://doi.org/10.1016/j.cose.2020.101889 Feng, C., Yu, K., Aloqaily, M., Alazab, M., Lv, Z., & Mumtaz, S. (2020). Attribute-based encryption with parallel outsourced decryption for edge intelligent IoV. IEEE Transactions on Vehicular Technology, 69(11), 13784–13795. https://doi.org/10.1109/TVT.2020.3027568 Firouzi,F.,Farahani,B.,&Marinsek,A.(2022).Theconvergenceandinterplayof edge, fog, and cloud in theAI-driven Internet of Things (IoT). Information Systems, 107,101840. https://doi.org/10.1016/j.is.2021.101840 Foukalas,F.,Tziouvaras,A.,&Tsiftsis,T.A.(2021).Anoverviewof enablingfederated learning over wireless networks. 2021 IEEE International Mediterranean Conference on Communications and Networking (IEEE Meditcom 2021),271– 276. https://doi.org/10.1109/MeditCom49071.2021.9647687 Frigo, A. L., Zentgraf, R.-D., & Bleninger, T. B. (2019). Two-dimensional vessel-current interaction model for inland waterways assessment. Journal of Waterway Port Coastal and Ocean Engineering, 145(1), 04018036. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000494 Fu,S.,Wang,X.,Tang,J.,Lan,S.,&Tian,Y.(2024).Generalizedrobustlossfunctions for machine learning. Neural Networks, 171, 200–214. https://doi.org/10.1016/j.neunet.2023.12.013 Fueller,J.,Hutter,K.,Wahl,J.,Bilgram,V.,&Tekic,Z. (2022).HowAIrevolutionizes innovation management—Perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change, 178, 121598. https://doi.org/10.1016/j.techfore.2022.121598 Furfaro,A.,&Nigro,L.(2003).Temporalverificationof communicatingreal-timestate machines using Uppaal. 2003 IEEE International Conference on Industrial Technology, Vols 1 and 2, Proceedings, 399–404. https://www.webofscience.com/wos/alldb/summary/b97b75c9-c558-40f1b9cf-61d716499fd1-60ca75c4/relevance/1 Galletta, A., Taheri, J., & Villari, M. (2019). On the applicability of secret share algorithms for saving data on IoT, edge and cloud devices. 2019 International Conference on Internet of Things and IEEE Green Computing And Communications and IEEE Cyber, Physical And Social Computing and IEEE Smart Data, 14–21. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00026 Gan, J., Zhang, L., Chen, H., Bai, L., Zhang, X., Yang, L., & Zhang, Y. (2023). Underground garage patrol based on road marking recognition by Keras and Tensorflow. Applied Sciences-Basel, 13(4), 2385. https://doi.org/10.3390/app13042385 Gao,H.,Huang,W.,Liu,T.,Yin,Y.,&Li,Y. (2023).PPO2: Location privacy-oriented task offloading to edge computing using reinforcement learning for intelligent autonomous transport systems. IEEE Transactions On Intelligent Transportation Systems, 24(7), 7599–7612. https://doi.org/10.1109/TITS.2022.3169421 Gao, M., Li, J., Di, X., Li, X., & Zhang, M. (2024).Ablind signature scheme for IoV based on 2D-SCML image encryption and lattice cipher. Expert Systems with Applications, 246,123215. https://doi.org/10.1016/j.eswa.2024.123215 Gao,Y.,Kim, M.,Abuadbba,S.,Kim,Y., Thapa,C., Kim, K., Camtep, S.A.,Kim, H., & Nepal, S. (2020). End-to-end evaluation of federated learning and split learning for internet of things. 2020 International Symposium on Reliable Distributed Systems (SRDS 2020), 91–100. https://doi.org/10.1109/SRDS51746.2020.00017 Gao, Y., Zhang, P., Li, Z., Zhou, C., Liu, Y., & Hu, Y. (2021). Heterogeneous graph neural architecture search. In J. Bailey, P. Miettinen, Y. S. Koh, D. Tao, & X. Wu (Eds.), 2021 21st IEEE International Conference on Data Mining (pp. 1066–1071).https://doi.org/10.1109/ICDM51629.2021.00124 Ge, X., Xiao, S., Han, Q.-L., Zhang, X.-M., & Ding, D. (2022). Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hocnetworks.IEEE-CAA Journal of Automatica Sinica,9(1), 31–46. https://doi.org/10.1109/JAS.2021.1004060 Goh,M.J.S.,Chiew,Y.S.,&Foo,J.J.(2020).Outlierpercentageestimationfor shape-and parameter-independent outlier detection. IET Image Processing, 14(14), 3414–3421. Gong, J., Kang, J., Simeone, O., & Kassab, R. (2022). Forget-svgd: Particle-based bayesian federated unlearning. 2022 IEEE Data Science and Learning Workshop.https://doi.org/10.1109/DSLW53931.2022.9820602 Goyal,H.,&Saha,S.(2022).Multi-partycomputationinIoTfor privacy-preservation. 2022 IEEE 42ND International Conference on Distributed Computing Systems, 1280–1281. https://doi.org/10.1109/ICDCS54860.2022.00133 Gu, K., Wang, K., Li, X., & Jia, W. (2022). Multi-fogs-based traceable privacy-preserving scheme for vehicular identity in internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(8), 12544–12561. https://doi.org/10.1109/TITS.2021.3115171 HaddadPajouh, H., Dehghantanha,A., Parizi, R. M.,Aledhari, M., & Karimipour, H. (2021).Asurvey on internet of things security: Requirements, challenges, and solutions. Internet of Things, 14, 100129. https://doi.org/10.1016/j.iot.2019.100129 Hajiheydari,N.,Talafidaryani,M.,& Khabiri,S. (2019).IoT big datavaluemap:How to generate value from IoT data. ICSLT 2019: Proceedings of the 5th International Conference on E-Society, E-Learning and E-Technologies, 98– 103. https://doi.org/10.1145/3312714.3312728 Han, S., Wang, F.-Y., Luo, G., Li, L., & Qu, F. (2023). Parallel surfaces: ServiceorientedV2Xcommunicationsfor autonomousvehicles. IEEE Transactions on Intelligent Vehicles, 8(11), 4536–4545. https://doi.org/10.1109/TIV.2023.3324507 Han, W., Cheng, M., Lei, M., Xii, H.,Yang,Y., & Qian, L. (2020). Privacy protection algorithm for the internet of vehicles based on local differential privacy and game model. Cmc-Computers Materials & Continua, 64(2), 1025–1038. https://doi.org/10.32604/cmc.2020.09815 Hao, S., Zhang, H., & Song, M. (2019). Big data, big data analytics capability, and sustainable innovation performance. Sustainability, 11(24), 7145. https://doi.org/10.3390/su11247145 Harrabi, S., Ben Jaafar, I., & Ghedira, K. (2023). Survey on IoV routing protocols. Wireless Personal Communications, 128(2), 791–811. https://doi.org/10.1007/s11277-022-09976-5 Hbaieb,A.,Ayed,S.,&Chaari,L.(2022).Asurveyof trustmanagementintheInternet of vehicles. Computer Networks, 203, 108558. https://doi.org/10.1016/j.comnet.2021.108558 He,J.,Wang,C.,&Chen,H.(2021).NewEnginetopromotebigdataindustryupgrade. InK.Arai(Ed.), Intelligent Computing, Vol 2 (Vol. 284,pp.232–248).Springer InternationalPublishingAg. https://doi.org/10.1007/978-3-030-80126-7_18 He, J., Zhang, S., Yang, M., Shan, Y., & Huang, T. (2022). BDCN: Bi-directional cascade network for perceptual edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1), 100–113. https://doi.org/10.1109/TPAMI.2020.3007074 He, W. (2017). Research on LBS privacy protection technology in mobile social networks. In B. Xu (Ed.), 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (pp. 73–76). IEEE. https://www.webofscience.com/wos/woscc/summary/dcfb67ba-5f09-4eae9b2f-aeb5f2647f67-60cc7d07/relevance/1 He,W., Huang,Z., Liang, M., Liang, S.,&Yang,H. (2021).Blending pruning criteria for convolutional neural networks. In I. Farkas, P. Masulli, S. Otte, & S. Wermter (Eds.),Artificial Neural Networks and Machine Learning—Icann 2021, Pt Iv (Vol. 12894, pp. 3–15). Springer International Publishing Ag. https://doi.org/10.1007/978-3-030-86380-7_1 Heidari, A., Navimipour, N. J., & Unal, M. (2022). Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies:Asystematicliteraturereview. Sustainable Cities and Society, 85, 104089. https://doi.org/10.1016/j.scs.2022.104089 Hinton,G.,Vinyals,O.,&Dean,J.(2015).Distilling the knowledge in a neural network (arXiv:1503.02531).arXiv. http://arxiv.org/abs/1503.02531 Horio, Y. (2019). Chaotic neural network reservoir. 2019 International Joint Conference on Neural Networks. https://www.webofscience.com/wos/woscc/summary/f500a5ac-2871-44818269-0b7beb6da9cf-d13141d0/relevance/1 Hou, X., Ren, Z., Wang, J., Cheng, W., Ren, Y., Chen, K.-C., & Zhang, H. (2020). Reliablecomputationoffloading for edge-computing-enabledsoftware-defined IoV. IEEE Internet of Things Journal, 7(8), 7097–7111. https://doi.org/10.1109/JIOT.2020.2982292 Hu,P.,Wang,Y.,Vajdi,A.,Gong,B.,&Wang,Y.(2021).Secure multi-subintervaldata aggregation scheme with interval privacy preservation for vehicle sensing systems. Journal of Circuits Systems and Computers, 30(16), 2150286. https://doi.org/10.1142/S0218126621502868 Hu,S.,Li,Q.,&He,B.(2023).Communication-efficientgeneralizedneuronmatching for federated learning. Proceedings of the 52nd International Conference on Parallel Processing, ICPP 2023, 254–263. https://doi.org/10.1145/3605573.3605726 Hu, X., Li, R., Wang, L., Ning,Y., & Ota, K. (2023).A data sharing scheme based on federated learning in IoV. IEEE Transactions on Vehicular Technology, 72(9), 11644–11656. https://doi.org/10.1109/TVT.2023.3266100 Huang, J., Xu, C., Ji, Z., Xiao, S., Liu, T., Ma, N., & Zhou, Q. (2022). AFLPC: An asynchronous federated learning privacy-preserving computing model applied to 5G-V2X. Security and Communication Networks, 2022, 9334943. https://doi.org/10.1155/2022/9334943 Huang, L., Feng, X., Feng, A., Huang, Y., & Qian, L. P. (2022). Distributed deep learning-based offloading for mobile edge computing networks. Mobile Networks & Applications, 27(3), 1123–1130. https://doi.org/10.1007/s11036018-1177-x Ilias, C., & Georgios, S. (2019). Machine learning for all: A more robust federated learning framework. In P. Mori, S. Furnell, & O. Camp (Eds.), Proceedings of the 5th International Conference on Information Systems Security and Privacy (pp. 544–551).Scitepress. https://doi.org/10.5220/0007571705440551 Ingram, E., Gursoy, F., & Kakadiaris, I. A. (2022). Accuracy, fairness, and interpretability of machine learning criminal recidivism models. 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies,233–241. https://doi.org/10.1109/BDCAT56447.2022.00040 Iqbal, M., Tariq, A., Adnan, M., Din, I. U., & Qayyum, T. (2023). FL-ODP: An optimized differential privacy enabled privacy preserving federated learning. IEEE ACCESS, 11, 116674–116683. https://doi.org/10.1109/ACCESS.2023.3325396 Jain,A., Rodriguez,D.,delAlamo,J. M.,& Sadeh,N. (2023). ATLAS:Automatically detectingdiscrepanciesbetweenprivacypoliciesand privacylabels.2023 IEEE European Symposium on Security And Privacy Workshops, 94–107. https://doi.org/10.1109/EuroSPW59978.2023.00016 Jiang, J. C., Kantarci, B., Oktug, S., & Soyata, T. (2020). Federated learning in smart city sensing: Challenges and opportunities. Sensors, 20(21), 6230. https://doi.org/10.3390/s20216230 Jiang,W.,&Liu,J. (2017).Researchon addressingtechnologyof Internetof things.In M. Zhu&X. Xia(Eds.), Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (Vol.150,pp.149–152).AtlantisPress. https://www.webofscience.com/wos/woscc/summary/a9624700-8726-4999b9ab-989086a95985-60cc12c8/relevance/1 Jorayeva, M., Akbulut, A., Catal, C., & Mishra, A. (2022). Machine learning-based software defect prediction for mobile applications: A systematic literature review. Sensors, 22(7),2551. https://doi.org/10.3390/s22072551 Kamma, K., Inoue, S., & Wada, T. (2022). Pruning ratio optimization with layer-wise pruning method for accelerating convolutional neural networks. IEICE Transactions on Information and Systems, E105D(1), 161–169. https://doi.org/10.1587/transinf.2021EDP7096 Kang, M., & Kang, S. (2021). Data-free knowledge distillation in neural networks for regression. Expert Systems with Applications, 175, 114813. https://doi.org/10.1016/j.eswa.2021.114813 Karim,H., &Rawat, D. B.(2022).TollsOnlyPlease-Homomorphic encryptionfor toll transponder privacy in Internet of vehicles. IEEE Internet of Things Journal, 9(4),2627–2636. https://doi.org/10.1109/JIOT.2021.3056240 Khare,S.,&Totaro, M. (2019).BigData inIoT. 2019 10TH International Conference on Computing, Communication and Networking Technologies. https://www.webofscience.com/wos/woscc/summary/32e039cb-7a34-4dafafb7-0a40e9b907db-d12813db/relevance/1 Kim,D.-Y.,Jung, M., &Kim, S. (2021).An Internetof Vehicles(IoV) accessgateway design considering the efficiency of the in-vehicle ethernet backbone. Sensors, 21(1),98. https://doi.org/10.3390/s21010098 Kim, K., Kim, J. S., Jeong, S., Park, J.-H., & Kim, H. K. (2021). Cybersecurity for autonomous vehicles: Review of attacks and defense. Computers & Security, 103,102150. https://doi.org/10.1016/j.cose.2020.102150 Konen, J., Mcmahan, H. B., Yu, F. X., P Richtárik, & Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. http://doc.paperpass.com/patent/arXiv161005492.html Kong,Q.,Lu,R.,Ma,M.,&Bao,H.(2019).Aprivacy-preservingsensorydatasharing scheme in Internet of Vehicles. Future Generation Computer Systems-the International Journal of Escience, 92, 644–655. https://doi.org/10.1016/j.future.2017.12.003 Kosinski, M. (2021). Facial recognition technology can expose political orientation from naturalistic facial images. Scientific Reports, 11(1), 100. https://doi.org/10.1038/s41598-020-79310-1 Ku, H., Susilo, W., Zhang, Y., Liu, W., & Zhang, M. (2022). Privacy-preserving federated learning in medical diagnosis with homomorphic re-encryption. Computer Standards & Interfaces, 80, 103583. https://doi.org/10.1016/j.csi.2021.103583 Kumar,C.P., & Selvakumar,R.(2018).Authentication protocolusingerror correcting codesandcyclicredundancycheck.InS.Chellappan,W.Cheng,&W.Li(Eds.), Wireless Algorithms, Systems, and Applications (WASA 2018) (Vol. 10874, pp. 874–882).Springer InternationalPublishingAg. https://doi.org/10.1007/978-3319-94268-1_80 Kuutti,S.,Bowden,R.,Jin,Y.,Barber,P.,&Fallah,S.(2021).Asurveyof deeplearning applications to autonomous vehicle control. IEEE Transactions on Intelligent Transportation Systems, 22(2), 712–733. https://doi.org/10.1109/TITS.2019.2962338 Laghari, A. A., Khan, A. A., Alkanhel, R., Elmannai, H., & Bourouis, S. (2023). Lightweight-BIoV: Blockchain distributed ledger technology (BDLT) for Internet of Vehicles (IoVs). Electronics, 12(3), 677. https://doi.org/10.3390/electronics12030677 Lahiri,P.K.,Das,D.,Mansoor,W.,Banerjee,S.,&Chatterjee,P. (2020).Atrustworthy blockchainbasedframeworkor impregnableIoVinedgecomputing.2020 IEEE 17TH International Conference on Mobile Ad Hoc And Smart Systems, 26–31. https://doi.org/10.1109/MASS50613.2020.00013 Le, C. P., Soltani, M., Ravier, R., & Tarokh, V. (2021). Task-awareneural architecture search. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing,4090–4094. https://doi.org/10.1109/ICASSP39728.2021.9414412 Lei, Y., Wang, S. L., Su, C., & Ng, T. F. (2022). OES-Fed: A federated learning frameworkinvehicular networkbasedonnoisedatafiltering. PeerJ Computer Science, 8,e1101. https://doi.org/10.7717/peerj-cs.1101 Lei, Y., Wang, S. L., Zhong, M., Wang, M., & Ng, T. F. (2022).A federated learning framework based on incremental weighting and diversity selection for internet of vehicles. Electronics, 11(22), Article 22. https://doi.org/10.3390/electronics11223668 Li,B.,Liang,R.,Zhou,W.,Yin,H.,Gao,H.,&Cai,K. (2022).LBS meetsblockchain: An efficient method with security preserving trust in SAGIN. IEEE Internet of Things Journal, 9(8),5932–5942. https://doi.org/10.1109/JIOT.2021.3064357 Li, B., Shi, Y., Guo, Y., Kong, Q., & Jiang, Y. (2022). Incentive and knowledge distillation based federated learning for Cross-Silo applications. IEEE Infocom 2022 -IEEE Conference on Computer Communications Workshops. https://doi.org/10.1109/INFOCOMWKSHPS54753.2022.9798320 Li, J., Ji, Y., Choo, K.-K. R., & Hogrefe, D. (2019). CL-CPPA: Certificate-Less Conditional Privacy-Preserving Authentication Protocol for the Internet of Vehicles. IEEE Internet of Things Journal, 6(6), 10332–10343. https://doi.org/10.1109/JIOT.2019.2938008 Li, M., Hsu, W., Xie, X., Cong, J., & Gao, W. (2020). SACNN: Self-Attention convolutional neural network for low-dose CT denoising with self-supervised perceptuallossnetwork. IEEE Transactions on Medical Imaging, 39(7),2289– 2301. https://doi.org/10.1109/TMI.2020.2968472 Li,M.,Shan,S.,Chandra,S. S.,Liu,F.,&Crozier,S. (2020).Fastgeometricdistortion correction using a deep neural network: Implementation for the 1 Tesla MRI-Linac system. Medical Physics, 47(9), 4303–4315. https://doi.org/10.1002/mp.14382 Li,T., Sahu,A. K.,Talwalkar,A., & Smith,V. (2020).Federated learning:Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50– 60. https://doi.org/10.1109/MSP.2020.2975749 Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2,429–450. Li,W.,&Plested,J. (2019).Pruningconvolutionalneuralnetworkwithdistinctiveness approach. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Neural Information Processing, Iconip 2019, Pt V (Vol. 1143,pp. 448–455).Springer International PublishingAg. https://doi.org/10.1007/978-3-030-36802-9_48 Li, W., Wang, Z., Sun, W., & Bahrami, S. (2023). An ensemble clustering framework based on hierarchical clustering ensemble selection and clusters clustering. Cybernetics and Systems, 54(5), 741–766. https://doi.org/10.1080/01969722.2022.2073704 Li, Y., He, Z., Gu, X., Xu, H., & Ren, S. (2024).AFedAvg: Communication-efficient federated learning aggregation with adaptive communication frequency and gradient sparse. Journal of Experimental & Theoretical Artificial Intelligence, 36(1),47–69. https://doi.org/10.1080/0952813X.2022.2079730 Li,Y.,Kim,S.,&Sy,E. (2021).A surveyonamazonAlexaattacksurfaces. 2021 IEEE 18th Annual Consumer Communications & Networking Conference. https://doi.org/10.1109/CCNC49032.2021.9369553 Li,Y.,Li,H.,Xu,G.,Xiang,T.,&Lu,R.(2022).Practicalprivacy-preservingfederated learning in vehicular fog computing. IEEE Transactions on Vehicular Technology, 71(5),4692–4705. https://doi.org/10.1109/TVT.2022.3150806 Li,Y., Li,K.,Wang, S., Chen, X., &Wen, D. (2022).Pilot behavior recognition based on multi-modality fusion technology using physiological characteristics. Biosensors-Basel, 12(6),404. https://doi.org/10.3390/bios12060404 Li,Y.,&Ren,F.(2020).BNNPruning:Pruningbinaryneuralnetworkguidedbyweight flippingfrequency. Proceedings of the Twentyfirst International Symposium on Quality Electronic Design (ISQED 2020), 306–310. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000614842000053 Li, Y., Wang, Y., & Guan, H. (2019). Improve the detection of clustered outliers via outlier score propagation. 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking,1085–1091. Li, Y., Zhou, Y., Jolfaei,A., Yu, D., Xu, G., & Zheng, X. (2021). Privacy-preserving federated learning framework based on chained secure multiparty computing. IEEE Internet of Things Journal, 8(8), 6178–6186. https://doi.org/10.1109/JIOT.2020.3022911 Li, Z. (2022).A personalized privacy-preserving scheme for federated learning. 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms,1352–1356. https://doi.org/10.1109/EEBDA53927.2022.9744805 Li, Z., Sharma, V., & Mohanty, S. P. (2020). Preserving data privacy via federated learning:Challengesandsolutions.IEEE Consumer Electronics Magazine,9(3), 8–16. https://doi.org/10.1109/MCE.2019.2959108 Lia, D., & Togan, M. (2020). Privacy-preserving machine learning using federated learning and secure aggregation. Proceedings of the 2020 12th International Conference on Electronics, Computers and Artificial Intelligence. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000627393500009 Liang, F., Yang, Q., Liu, R., Wang, J., Sato, K., & Guo, J. (2022). Semi-synchronous federated learning protocol with dynamic aggregation in internet of vehicles. IEEE Transactions on Vehicular Technology, 71(5), 4677–4691. https://doi.org/10.1109/TVT.2022.3148872 Lim, W. Y. B., Huang, J., Xiong, Z., Kang, J., Niyato, D., Hua, X.-S., Leung, C., & Miao, C. (2021). Towards federated learning in UAV-Enabled Internet of vehicles:Amulti-dimensional contract-matching approach. IEEE Transactions on Intelligent Transportation Systems, 22(8), 5140–5154. https://doi.org/10.1109/TITS.2021.3056341 Lim, W. Y. B., Xiong, Z., Niyato, D., Huang, J., Hua, X.-S., & Miao, C. (2020). Incentive mechanism design for federated learning in the Internet of vehicles. 2020 IEEE 92nd Vehicular Technology Conference. https://doi.org/10.1109/VTC2020-Fall49728.9348486 Lin, X., Wu, J., Mumtaz, S., Garg, S., Li, J., & Guizani, M. (2021). Blockchain-based on-demand computing resource trading in IoV-assisted smart city. IEEE Transactions on Emerging Topics in Computing, 9(3), 1373–1385. https://doi.org/10.1109/TETC.2020.2971831 Liu, C., Feng, W., Chen, Y., Wang, C.-X., & Ge, N. (2021). Cell-free satellite-UAV networks for 6G wide-area Internet of things. IEEE Journal on Selected Areas in Communications, 39(4), 1116–1131. https://doi.org/10.1109/JSAC.2020.3018837 Liu, F., Liu, J., & Feng, Y. (2021). Incremental-data stealth-transmission method in DSSS. Wireless Networks, 27(4), 2441–2449. https://doi.org/10.1007/s11276021-02590-6 Liu, J., Xu, H., Wang, L., Xu, Y., Qian, C., Huang, J., & Huang, H. (2023).Adaptive asynchronousfederatedlearninginresource-constrainededgecomputing.IEEE Transactions on Mobile Computing, 22(2), 674–690. https://doi.org/10.1109/TMC.2021.3096846 Liu,J.,Yang,D., Zhou,Y.,Zhang,G.,Xing,G., Liu,Y., Ma,Y.,Terasaki,O.,Yang,S., & Chen, L. (2021). Tricycloquinazoline-based 2D conductive metal-organic frameworks as promising electrocatalysts for CO2 reduction. Angewandte Chemie-International Edition, 60(26), 14473–14479. https://doi.org/10.1002/anie.202103398 Liu, R., & Pan, J. (2024). CRS:Aprivacy-preserving two-layered distributed machine learningframeworkfor IoV.IEEE Internet of Things Journal,11(1),1080–1095. https://doi.org/10.1109/JIOT.2023.3287799 Liu,R.W.,Nie,J.,Garg,S.,Xiong,Z.,Zhang,Y.,&Hossain,M.S.(2021).Data-driven trajectory quality improvement for promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems. IEEE Internet of Things Journal, 8(7), 5374–5385. https://doi.org/10.1109/JIOT.2020.3028743 Liu,S.,Liu,Z.,Xu,Z.,Liu,W.,&Tian,J. (2023).Hierarchicaldecentralizedfederated learning framework with adaptive clustering: Bloom-filter-based companions choice for learning Non-IID data in IoV. Electronics, 12(18), 3811. https://doi.org/10.3390/electronics12183811 Liu, X., Ma, Q., & Fu, X. (2014). Mapping a real-time system graphics design model to windows CE. In K. M. Lee, P. Yarlagadda, & Y. M. Lu (Eds.), Progress in Mechatronics and Information Technology, Pts 1 and 2 (Vols. 462–463, pp. 920–923). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMM.462-463.920 Liu,X.,Ma,W.,Yu,J.,Yu,K.,&Xiang,J.(2021).ASecret-sharing-basedsecuritydata aggregationschemein wirelesssensor networks. InZ. Liu,F. Wu,& S. K. Das (Eds.),Wireless Algorithms, Systems, and Applications, WASA 2021, PT II (Vol. 12938, pp. 303–313). Springer International Publishing Ag. https://doi.org/10.1007/978-3-030-86130-8_24 Lo, S. K., Lu, Q., Zhu, L., Paik, H.-Y., Xu, X., & Wang, C. (2022). Architectural patterns for the design of federated learning systems. Journal of Systems and Software, 191,111357. https://doi.org/10.1016/j.jss.2022.111357 Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2020). Blockchain empoweredasynchronousfederated learningfor securedatasharing in internet of vehicles. IEEE Transactions on Vehicular Technology, 69(4), 4298–4311. https://doi.org/10.1109/TVT.2020.2973651 Luo, J., &Wu, S. (2022). Fedsld: Federated learningwith shared label distribution for medical image classification. 2022 IEEE International Symposium on Biomedical Imaging.https://doi.org/10.1109/ISBI52829.2022.9761404 Luo, X., Zhao, Z., & Peng, M. (2021). Tradeoff between model accuracy and cost for federated learning in the mobile edge computing systems. 2021 IEEE Wireless Communications and Networking Conference Workshops. https://doi.org/10.1109/WCNCW49093.2021.9419985 Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020). Towardsfair andprivacy-preservingfederateddeepmodels.IEEE Transactions on Parallel and Distributed Systems, 31(11), 2524–2541. https://doi.org/10.1109/TPDS.2020.2996273 Ma, J., Naas, S.-A., Sigg, S., & Lyu, X. (2022). Privacy-preserving federated learning based on multi-key homomorphic encryption. International Journal of Intelligent Systems, 37(9),5880–5901. https://doi.org/10.1002/int.22818 Ma, X., Zhou,Y., Wang, L., & Miao, M. (2022). Privacy-preserving Byzantine-robust federated learning. Computer Standards & Interfaces, 80, 103561. https://doi.org/10.1016/j.csi.2021.103561 Manias,D.M.,&Shami,A.(2021).Makinga casefor federatedlearningintheinternet of vehiclesandintelligenttransportationsystems.IEEE Network, 35(3),88–94. https://doi.org/10.1109/MNET.011.2000560 Mao,Y., Xiao, Z., Lin, C.-T., de Gusmao, P. P. B., Lane, N. D., Zach, C., &Alibeigi, M. (2023).Decentralized trainingof 3D lanedetectionwith automaticlabeling using HD maps. 2023 IEEE 97TH Vehicular Technology Conference. https://doi.org/10.1109/VTC2023-Spring57618.2023.10199451 Matsuda,K., Sasaki,Y., Xiao, C.,& Onizuka,M. (2024).Benchmarkfor personalized federated learning. IEEE Open Journal of the Computer Society, 5, 2–13. https://doi.org/10.1109/OJCS.2023.3332351 Matsumoto, M., & Oguchi, M. (2021). Speeding up encryption on IoT devices using homomorphic encryption. 2021 IEEE International Conference on Smart Computing, 270–275. https://doi.org/10.1109/SMARTCOMP52413.2021.00059 Maurya, C., & Chaurasiya, V. K. (2023). Efficient Anonymous batch authentication scheme with conditional privacy in the Internet of Vehicles (IoV) applications. IEEE Transactions on Intelligent Transportation Systems, 24(9), 9670–9683. https://doi.org/10.1109/TITS.2023.3271355 McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data (arXiv:1602.05629; Version 2). arXiv. https://doi.org/10.48550/arXiv.1602.05629 Mehmood, I., Shahid, S., Hussain, H., Khan, I.,Ahmad, S., Rahman, S., Ullah, N., & Huda, S. (2023). A novel approach to improve software defect prediction accuracy using machine learning. IEEE Access, 11, 63579–63597. https://doi.org/10.1109/ACCESS.2023.3287326 Mei, Q., Xiong, H., Chen, J., Yang, M., Kumari, S., & Khan, M. K. (2021). Efficient certificatelessaggregatesignaturewithconditionalprivacypreservationinIoV. IEEE Systems Journal, 15(1), 245–256. https://doi.org/10.1109/JSYST.2020.2966526 Mei, Q., Xiong, H., Zhao, Y., & Yeh, K.-H. (2021). Toward blockchain-enabled IoV with edge computing: Efficient and privacy-preserving vehicular communication and dynamic updating. 2021 IEEE Conference on Dependable and Secure Computing.https://doi.org/10.1109/DSC49826.2021.9346240 Miao, L., Jiang, D., & Zhang, H. (2023). Wireless secret sharing game for Internet of things. Sustainability, 15(9),7427. https://doi.org/10.3390/su15097427 Miao, Q., Lin, H., Wang, X., & Hassan, M. M. (2021). Federated deep reinforcement learning based secure data sharing for Internet of things. Computer Networks, 197,108327. https://doi.org/10.1016/j.comnet.2021.108327 Miglietta, F., Griguolo, G., Bottosso, M., Giarratano, T., Lo Mele, M., Fassan, M., Cacciatore,M.,Genovesi,E.,DeBartolo,D.,Vernaci,G.,Amato,O.,Conte,P., Guarneri, V., & Dieci, M. V. (2021). Evolution of HER2-low expression from primary to recurrent breast cancer. NPJ Breast Cancer, 7(1), 137. https://doi.org/10.1038/s41523-021-00343-4 Miglietta, F., Griguolo, G., Bottosso, M., Giarratano, T., Lo Mele, M., Fassan, M., Cacciatore,M.,Genovesi,E.,DeBartolo,D.,Vernaci,G.,Amato,O.,Porra, F., Conte,P.,Guarneri,V.,&Dieci,M.V.(2022).HER2-low-positivebreastcancer: Evolution from primary tumor to residual disease after neoadjuvant treatment. NPJ Breast Cancer, 8(1),66. https://doi.org/10.1038/s41523-022-00434-w Mills,J.,Hu,J.,& Min,G. (2022).Multi-taskfederated learningfor personaliseddeep neural networks in edge computing. IEEE Transactions on Parallel and Distributed Systems, 33(3), 630–641. https://doi.org/10.1109/TPDS.2021.3098467 Mollah,M. B.,Zhao,J.,Niyato,D.,Guan,Y. L.,Yuen,C.,Sun,S.,Lam,K.-Y.,&Koh, L. H. (2021). Blockchain for the Internet of vehicles towards intelligent transportationsystems:Asurvey. IEEE Internet of Things Journal, 8(6),4157– 4185. https://doi.org/10.1109/JIOT.2020.3028368 Morales,G. D. F.,Bifet,A.,Khan,L.,Gama,J.,&Fan,W. (2016).IoT bigdatastream mining. KDD’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2119–2120. https://doi.org/10.1145/2939672.2945385 Morano, F., Raimondi, A., Pagani, F., Lonardi, S., Salvatore, L., Cremolini, C., Murgioni, S., Randon, G., Palermo, F., Antonuzzo, L., Pella, N., Racca, P., Prisciandaro, M., Niger, M., Corti, F., Bergamo, F., Zaniboni, A., Ratti, M., Palazzo, M., … Pietrantonio, F. (2022). Temozolomide followed by combination with low-dose Ipilimumab and Nivolumab in Patients With microsatellite-stable, O6-Methylguanine-DNA Methyltransferase-Silenced Metastatic Colorectal Cancer: The MAYA trial. Journal of Clinical Oncology, 40(14), 1562-+. https://doi.org/10.1200/JCO.21.02583 Mothukuri,V.,Parizi,R. M., Pouriyeh,S.,Huang,Y.,Dehghantanha,A.,&Srivastava,G. (2021). A survey on security and privacy of federated learning. Future Generation Computer Systems-the International Journal of Escience,115,619– 640. https://doi.org/10.1016/j.future.2020.10.007 Mugunthan, V., Peraire-Bueno, A., & Kagal, L. (2020). PrivacyFL: A simulator for privacy-preservingandsecurefederatedlearning. CIKM ’20: Proceedings ofthe 29th ACM International Conference on Information & Knowledge Management, 3085–3092. https://doi.org/10.1145/3340531.3412771 Muhammad, A., Saqib, M., & Song, W.-C. (2021). Sensor virtualization and data orchestration in Internet of vehicles (IoV). In T.Ahmed, O. Festor,Y. GhamriDoudane,J. M. Kang,A. E. Schaeffer-Filho,A. Lahmadi,&E. Madeira(Eds.), 2021 IFIP/IEEE International Symposium on Integrated Network Management (pp. 998–1003). https://www.webofscience.com/wos/woscc/summary/08a7d42b-0094-4ad88698-cdc3a1aff1b0-d12d2903/relevance/1 Muralidhar,N., Islam, M. R., Marwah, M., Karpatne,A., & Ramakrishnan,N. (2018). Incorporatingprior domainknowledgeintodeepneuralnetworks.InN.Abe,H. Liu,C. Pu,X. Hu, N.Ahmed,M. Qiao,Y. Song,D. Kossmann,B. Liu, K. Lee, J. Tang, J. He, & J. Saltz (Eds.), 2018 IEEE International Conference on Big Data (pp. 36–45). https://www.webofscience.com/wos/woscc/fullrecord/WOS:00046849930000 9 Nakayama, A., & Hagiwara, M. (2020). The first quantum error-correcting code for single deletion errors. IEICE Communications Express, 9(4), 100–104. https://doi.org/10.1587/comex.2019XBL0154 Natella, R., Ceccarelli,A., & Ficco, M. (2022). Federated and generative data sharing for data-drivensecurity:Challengesandapproach.InG.Fortino,R.Gravina,A. Guerrieri, & C. Savaglio (Eds.), 2022 IEEE Intl Conf on Dependable, Autonomic And Secure Computing, Intl Conf on Pervasive Intelligence And Computing, Intl Conf on Cloud And Big Data Computing, Intl Conf on Cyber Science and Technology Congress (pp. 410–415). https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927754 Naveen, D., & Praveen, K. (2019). PUF authentication using visual secret sharing scheme. 2019 5TH International Conference on Advanced Computing & Communication Systems, 472–475. https://doi.org/10.1109/icaccs.2019.8728504 Nemiwal, M., Gosu,V., Zhang,T. C., & Kumar,D. (2021).Metalorganicframeworks as electrocatalysts: Hydrogen evolution reactions and overall water splitting. International Journal of Hydrogen Energy, 46(17), 10216–10238. https://doi.org/10.1016/j.ijhydene.2020.12.146 Ni,W.,Zhu,S.,Karim,M.M.,Asheralieva,A.,Kang,J.,Xiong,Z.,&Maple,C.(2022). Lagrange coded federated learning (L-CoFL) model for Internet of vehicles. 2022 IEEE 42nd International Conference n Distributed Computing Systems, 864–872. https://doi.org/10.1109/ICDCS54860.2022.00088 NicholsonO, S., Hammell, R. J., Chakraborty, J., & Ali-Gombe, A. (2022). User awarenessand privacyregarding instantgamesonfacebook. InG. Meiselwitz, A. Moallem, P. Zaphiris, A. Ioannou, R. A. Sottilare, J. Schwarz, & X. Fang (Eds.), Hci International 2022—Late Breaking Papers: Interaction in New Media, Learning and Games (Vol. 13517, pp. 623–641). https://doi.org/10.1007/978-3-031-22131-6_46 Nie,Z., Su,C., Mao,Y.,& Bian,K. (2022). IoTPass: IoTdatamanagementsystemfor processingtime-seriesdata. 2022 Tenth International Conference on Advanced Cloud and Big Data,288–293.https://doi.org/10.1109/CBD58033.2022.00058 Nilsson,A.,Smith,S.,Ulmt,G.,Gustaysson,E.,&Jirstrand,M.(2018).Aperformance evaluation of federated learning algorithms. DIDL’18: Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning, 1–8. https://doi.org/10.1145/3286490.3286559 Ning,Z.,Sun,S.,Wang,X.,Guo,L.,Guo,S.,Hu,X.,Hu,B.,&Kwok,R.Y. K.(2022). Blockchain-enabled intelligent transportation systems: A distributed crowdsensing framework. IEEE Transactions on Mobile Computing, 21(12), 4201–4217. https://doi.org/10.1109/TMC.2021.3079984 Ning, Z., Zhang, K., Wang, X., Guo, L., Hu, X., Huang, J., Hu, B., & Kwok, R.Y. K. (2021). Intelligent edge computing in Internet of vehicles:Ajoint computation Onan,A.(2022).Bidirectionalconvolutionalrecurrentneuralnetworkarchitecturewith group-wise enhancement mechanism for text sentiment classification. Journal of King Saud University-Computer and Information Sciences,34(5),2098–2117. https://doi.org/10.1016/j.jksuci.2022.02.025 offloading and caching solution. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2212–2225. https://doi.org/10.1109/TITS.2020.2997832 Östergård,P. R. J. (2005).Two newfour-error-correctingbinary codes. Designs Codes and Cryptography, 36(3), 327–329. https://doi.org/10.1007/s10623-004-17233 Pan,B.,Guo,H.,You,X.,&Xu,L. (2022).Privacy ratingof mobileapplicationsbased on crowdsourcing and machine learning. Journal of Global Information Management, 30(3).https://doi.org/10.4018/JGIM.20220701.oa5 Pan, K., He, D., & Xu, C. (2021). Local model privacy-preserving study forfederated learning.InJ.GarciaAlfaro,S. Li,R. Poovendran,H.Debar,&M.Yung(Eds.), Security and Privacy in Communication Networks, Securecomm 2021, PT I (Vol. 398, pp. 287–307). Springer International Publishing Ag. https://doi.org/10.1007/978-3-030-90019-9_15 Pan, Q., Tong, H., Lv, J., Luo, T., Zhang, Z., Yin, C., & Li, J. (2023). Image segmentation semantic communication over internet of vehicles. 2023 IEEE Wireless Communications And Networking Conference. https://doi.org/10.1109/WCNC55385.2023.10118717 Pereira, J. W., & Ribeiro, M. X. (2022). Hyperparameter for deep learning applied in mammogram image classification. In L. Shen,A. R. Gonzalez, K. C. Santosh, Z. Lai, R. Sicilia, J. R. Almeida, & B. Kane (Eds.), 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (pp. 90–95). IEEE. https://doi.org/10.1109/CBMS55023.2022.00023 Pillutla,K., Kakade,S. M., & Harchaoui,Z. (2022).RobustAggregationfor federated learning. IEEE Transactions on Signal Processing, 70, 1142–1154. https://doi.org/10.1109/TSP.2022.3153135 Pokhrel, S. R., & Choi, J. (2020). Improving TCP performance over WiFi for Internet of vehicles: A federated learning approach. IEEE Transactions on Vehicular Technology, 69(6),6798–6802. https://doi.org/10.1109/TVT.2020.2984369 Polap, D., & Wozniak, M. (2022).Ahybridization of distributed policy and heuristic augmentationfor improvingfederatedlearningapproach. Neural Networks,146, 130–140. https://doi.org/10.1016/j.neunet.2021.11.018 Popa,C.-A.(2016).Matrix-Valuedhopfieldneuralnetworks.InL.Cheng,Q.Liu,&A. Ronzhin(Eds.),Advances In Neural Networks -ISNN 2016 (Vol.9719,pp.127– 134). Springer International PublishingAg. https://doi.org/10.1007/978-3-31940663-3_15 Posner, J., Tseng, L., Aloqaily, M., & Jararweh, Y. (2021). Federated learning in vehicular networks: Opportunities and solutions. IEEE Network, 35(2), 152– 159. Procaccio, L., Bergamo, F., Gatti, M., Chiusole, B., Tierno, G., Bergo, E., Daniel, F., Nappo,F.,Maddalena,G., Rasola, C., Barsotti,G., DeGrandis, M. C., Piva, V. M., Rizzato, M. D., Sergi, G., Brunello,A., Zagonel, V., & Lonardi, S. (2022). The oncological multidimensional prognostic index is a promising decision-making tool:Areal-world analysis in older patients with metastatic colorectal cancer. European Journal of Cancer, 177, 112–119. https://doi.org/10.1016/j.ejca.2022.09.023 Qi, Y., Hossain, M. S., Nie, J., & Li, X. (2021). Privacy-preserving blockchain-based federated learning for traffic flow prediction. Future Generation Computer Systems-The International Journal of Escience, 117, 328–337. https://doi.org/10.1016/j.future.2020.12.003 Rahajoe, A. D. (2019). Forecasting feature selection based on single exponential smoothing using Wrapper method. International Journal of Advanced Computer Science and Applications, 10(6). Rajabzadeh, P., &Outtagarts,A. (2023).Federated learningfor distributedNWDAF architecture.InD.Lopez,M.J.Montpetit,W.Cerroni,M.DiMauro,&P.Borylo (Eds.), 2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops. IEEE. https://doi.org/10.1109/ICIN56760.2023.10073493 Rao, N. S., Imam, N., Liu, Z., Kettimuthu, R., & Foster, I. (2020). Machine learning methodsfor connectionRTTand lossrateestimationusing MPI measurements under random losses. In S. Boumerdassi, E. Renault, & P. Muhlethaler (Eds.), Machine Learning for Networking (MLN 2019) (Vol. 12081, pp. 154–174). Springer International Publishing Ag. https://doi.org/10.1007/978-3-03045778-5_11 Rasouli, P., & Yu, I. C. (2021). Explainable debugger for black-box machine learning models. 2021 International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN52387.2021.9533944 Rathee, G., Iqbal, R., & Khelifi, A. (2021). Decision making in internet of vehicles using pervasive trusted computing scheme. Cmc-Computers Materials & Continua, 68(2),2755–2769. https://doi.org/10.32604/cmc.2021.017000 Ray, P. P., & Nguyen, K. (2020).A review on blockchain for medical delivery drones in 5G-IoT era: Progress and challenges. 2020 IEEE/Cic International Conference on Communications in China, 29–34. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000681582200006 Reddy,K. R.,& Muralidhar,A. (2023).Machine learning-basedroadsafetyprediction strategiesfor Internetof vehicles(IoV) enabledvehicles:Asystematicliterature review. IEEE Access, 11, 112108–112122. https://doi.org/10.1109/ACCESS.2023.3315852 Ren, H., Cheng, Z., Qin, J., & Lu, R. (2023). Deception attacks on event-triggered distributed consensus estimation for nonlinear systems. Automatica, 154, 111100. https://doi.org/10.1016/j.automatica.2023.111100 Ren,W.,Tong,X.,Du,J.,Wang,N.,Li,S.C.,Min,G.,Zhao,Z.,&Bashir,A.K.(2021). Privacy-preserving using homomorphic encryption in Mobile IoT systems. Computer Communications, 165, 105–111. https://doi.org/10.1016/j.comcom.2020.10.022 Reunanen, N., Räty, T., & Lintonen, T. (2020). Automatic optimization of outlier detection ensembles using a limited number of outlier examples. International Journal of Data Science and Analytics, 10(4),377–394. Ribeiro, A. H., & Schon, T. B. (2023). Overparameterized linear regression under adversarial attacks. IEEE Transactions on Signal Processing, 71, 601–614. https://doi.org/10.1109/TSP.2023.3246228 Rodriguez-Barroso, N., Stipcich, G., Jimenez-Lopez, D., Antonio Ruiz-Millan, J., Martinez-Camara, E., Gonzalez-Seco, G., Victoria Luzon, M., Veganzones,M. A., & Herrera, F. (2020). Federated learning anddifferential privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preservingdataprivacy. Information Fusion, 64,270–292. Rossini, P., Stromer-Galley, J., Baptista, E. A., & Veiga de Oliveira, V. (2021). Dysfunctional information sharing on WhatsApp and Facebook: The role of political talk, cross-cutting exposure and social corrections. New Media & Society, 23(8),2430–2451. https://doi.org/10.1177/1461444820928059 Russell, J. (2021). Machine learning equity and accuracy in an applied justice setting. 2021 IEEE International Conference on Smart Computing, 215–221. https://doi.org/10.1109/SMARTCOMP52413.2021.00050 Ruzafa-Alcazar, P., Fernandez-Saura, P., Marmol-Campos, E., Gonzalez-Vidal, A., Hernandez-Ramos, J. L., Bernal-Bernabe, J., & Skarmeta, A. F. (2023). Intrusion detection based on privacy-preserving federated learning for the industrialIoT. IEEE Transactions on Industrial Informatics, 19(2),1145–1154. https://doi.org/10.1109/TII.2021.3126728 Saha, S., Hota, A., Choudhury, B., Nag, A., & Nandi, S. (2023). NTRU and secret sharing based secure group communication for IoT applications. IEEE Access, 11,117341–117350. https://doi.org/10.1109/ACCESS.2023.3325305 Saiz-Rubio,V.,&Rovira-Mas,F. (2020).From smartfarmingtowardsagriculture5.0: A review on crop data management. Agronomy-Basel, 10(2), 207. https://doi.org/10.3390/agronomy10020207 Saleem, M. U., & Fan, L. (2023). Private Data synthesis from decentralized non-IID data. 2023 International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN54540.2023.10191553 Saputra,Y. M., Hoang, D. T., Nguyen, D. N., Tran, L.-N., Gong, S., & Dutkiewicz, E. (2023).Dynamicfederatedlearning-basedeconomicframeworkfor Internet-ofvehicles. IEEE Transactions on Mobile Computing, 22(4), 2100–2115. https://doi.org/10.1109/TMC.2021.3122436 Sarma, K., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman,A. G., Kulkarni, R.,Wood, B. J., Choyke,P. L., Priester,A. M., Marks,L. S.,Raman,S. S.,Enzmann,D.,Turkbey,B., Speier,W.,&Arnold,C. W. (2021). Federated learning improves site performance in multicenter deep learning without data sharing. Journal of the American Medical Informatics Association, 28(6),1259–1264. https://doi.org/10.1093/jamia/ocaa341 Sassi, M. S. H., & Fourati, L. C. (2020). Investigation on deep learning methods for privacy and security challenges of cognitive IoV. 2020 16th International Wireless Communications & Mobile Computing Conference, 714–720. https://www.webofscience.com/wos/woscc/summary/cc118b04-db5c-4087badc-b76fcf08d1de-d0d9445c/relevance/1 Schak,M., &Gepperth,A. (2019).AStudyon CatastrophicForgetting inDeepLSTM Networks. In I. V. Tetko, V. Kurkova, P. Karpov, & F. Theis (Eds.), Artificial neural networks and machine learning—ICANN 2019: Deep learning, Pt Ii (Vol. 11728, pp. 714–728). Springer International Publishing Ag. https://doi.org/10.1007/978-3-030-30484-3_56 Searle, R., Gururaj, P., Gaikwad, S., & Kannur, K. (2023). Secure federated machine learningwithflexibletopologyanddistributedprivacycontrols.InM. Blowers, J. Holt, & B. T. Wysocki (Eds.), Disruptive Technologies in Information Sciences VII (Vol. 12542, p. 125420C). Spie-Int Soc Optical Engineering. https://doi.org/10.1117/12.2663767 Seth,R., Swain,S. K.,& Mishra, S. K. (2018).Singleobjecttracking usingestimation algorithms. 2018 2nd International Conference on Power, Energy and Environment: Towards Smart Technology (ICEPE), 1–6. Shen, L. (2019). A self-adaptive feedback handoff algorithm based decision tree for Internetof vehicles. Jun Zheng· Wei Xiang Pascal Lorenz· Shiwen Mao,177. Shen, S., Zhu, T., Wu, D., Wang, W., & Zhou, W. (2022). From distributed machine learning to federated learning: In the view of data privacy and security. Concurrency and Computation-Practice & Experience, 34(16). https://doi.org/10.1002/cpe.6002 Shen, Y., Sowmya, A., Luo, Y., Liang, X., Shen, D., & Ke, J. (2023). A federated learning system for histopathology image analysis with an orchestral stainnormalizationGAN.IEEE Transactions on Medical Imaging,42(7),1969–1981. https://doi.org/10.1109/TMI.2022.3221724 Sheng, Y.-B., Zhou, L., & Long, G.-L. (2022). One-step quantum secure direct communication. Science Bulletin, 67(4), 367–374. https://doi.org/10.1016/j.scib.2021.11.002 Shin,J. Y., & Kang,D.-K. (2023).A higher performingDARTSmodel for CIFAR-10. InH.Zaynidinov,M.Singh,U.S.Tiwary,&D. Singh(Eds.), Intelligent Human Computer Interaction (pp. 95–99). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-27199-1_10 Shin, S., Boo, Y., & Sung, W. (2020). Knowledge distillation for optimization of quantized deep neural networks. 2020 IEEE Workshop on Signal Processing Systems, 111–116. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000783760500020 Shobanadevi,A., & Maragatham, G. (2017). Data mining techniques for IoT and big data -A survey. Proceedings of the International Conference on Intelligent Sustainable Systems (ICISS 2017), 66–78. https://www.webofscience.com/wos/woscc/summary/32e039cb-7a34-4dafafb7-0a40e9b907db-d12813db/relevance/1 Shu, W., Yan, Z., Chen, T., Yu, J., & Qian, W. (2022). Information granularity-based incremental feature selection for partially labeled hybrid data. Intelligent Data Analysis, 26(1),33–56. https://doi.org/10.3233/IDA-205560 Singh, D., Tripathi, G., & Jara, A. J. (2014). A survey of Internet-of-things: Future vision, architecture, challenges and services. 2014 IEEE World Forum on Internet of Things, 287–292. https://www.webofscience.com/wos/woscc/summary/a9624700-8726-4999b9ab-989086a95985-60cc12c8/relevance/1 Singh,V.K.,Singh,P.,Karmakar,M.,Leta,J.,&Mayr,P.(2021).Thejournalcoverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics,126(6),5113–5142.https://doi.org/10.1007/s11192-021-039485 Song, H. M., Woo, J., & Kim, H. K. (2020). In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications, 21, 100198. https://doi.org/10.1016/j.vehcom.2019.100198 Song, T., Tong, Y., & Wei, S. (2019). Profit Allocation for Federated Learning. In C. Baru,J. Huan,L. Khan,X. H. Hu,R.Ak,Y. Tian,R.Barga,C.Zaniolo,K. Lee, &Y. F.Ye(Eds.), 2019 IEEE International Conference on Big Data (pp. 2577– 2586).IEEE. https://doi.org/10.1109/bigdata47090.2019.9006327 Song, Y., Zheng, Q., Liu, B., & Gao, X. (2023). EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 710–719. https://doi.org/10.1109/TNSRE.2022.3230250 Stoyanova,M.,Nikoloudakis,Y.,Panagiotakis,S.,Pallis,E.,&Markakis,E. K.(2020). ASurveyontheInternetof things(IoT) forensics:Challenges,approaches,and open issues. IEEE Communications Surveys and Tutorials, 22(2), 1191–1221. https://doi.org/10.1109/COMST.2019.2962586 Su,X., Huo,Y., Wang,X., & Jing,T. (2023).An enhancingsemi-supervisedfederated learning framework for Internet of vehicles. 2023 IEEE 98th Vehicular Technology Conference, VTC2023-FALL. https://doi.org/10.1109/VTC2023Fall60731.2023.10333466 Sun,W., Chen, S., Huang,L., So, H. C.,& Xie, M. (2021). Deep convolutionalneural network compression via coupled tensor decomposition. IEEE Journal of Selected Topics in Signal Processing, 15(3), 603–616. https://doi.org/10.1109/JSTSP.2020.3038227 Sun, W., Lei, S., Wang, L., Liu, Z., & Zhang, Y. (2021). Adaptive federated learning and digital twin for industrial Internet of things. IEEE Transactions on Industrial Informatics, 17(8), 5605–5614. https://doi.org/10.1109/TII.2020.3034674 Sun,Y.,Chong,N.S.T.,&Ochiai,H.(2021).Informationstealinginfederatedlearning systems based on generative adversarial networks. 2021 IEEE International Conference on Systems, Man, and Cybernetics, 2749–2754. https://doi.org/10.1109/SMC52423.2021.9658652 Suzuki, J., Lameh, S. F., &Amannejad,Y. (2021). Using transfer learning in building federated learning models on edge devices. In M. Alsmirat, Y. Jararweh, F. Awaysheh, & M. Aloqaily (Eds.), 2021 Second International Conference on Intelligent Data Science Technologies and Applications (pp. 105–113). IEEE. https://doi.org/10.1109/IDSTA53674.2021.9660819 Szwarcman, D., Civitarese, D., & Vellasco, M. (2019). Quantum-Inspired neural architecture search. 2019 International Joint Conference on Neural Networks. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000530893805128 Takbiri, N., Houmansadr, A., Goeckel, D. L., & Pishro-Nik, H. (2020). Privacy of Dependentusersagainststatisticalmatching.IEEE Transactions on Information Theory, 66(9),5842–5865. https://doi.org/10.1109/TIT.2020.2985059 Tang, H., Wu, H., Qu, G., & Li, R. (2023). Double deep Q-network based dynamic framingoffloadinginvehicular edgecomputing.IEEE Transactions on Network Science and Engineering, 10(3), 1297–1310. https://doi.org/10.1109/TNSE.2022.3172794 Tang,Z. (2021).Secretsharing-basedIoTtextdataoutsourcing:Asecureandefficient scheme. IEEE Access, 9, 76908–76920. https://doi.org/10.1109/ACCESS.2021.3075282 Taslimasa,H.,Dadkhah,S.,Neto,E.C.P.,Xiong,P.,Ray,S.,&Ghorbani,A.A.(2023). Security issues in Internet of vehicles (IoV):Acomprehensive survey. Internet of Things, 22, 100809. https://doi.org/10.1016/j.iot.2023.100809 Tawalbeh, L., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020). IoT privacy and security: Challenges and solutions. Applied Sciences-Basel, 10(12), 4102. https://doi.org/10.3390/app10124102 Thanh-Tung,H.,&Tran,T.(2020).CatastrophicforgettingandmodecollapseinGANs. 2020 International Joint Conference on Neural Networks. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000626021404087 Thonglek, K., Takahashi, K., Ichikawa, K., Nakasan, C., Leelaprute, P., & Iida, H. (2022). Sparse communication for federated learning. In L. Mashayekhy, S. Schulte,V.Cardellini,B.Kantarci,Y.Simmhan,&B.Varghese(Eds.), 6th IEEE International Conference on Fog and Edge Computing (ICFEC 2022) (pp. 1– 8).IEEEComputer Soc. https://doi.org/10.1109/ICFEC54809.2022.00008 Timofeev, A. L., & Sultanov, A. K. (2019). Holographic method of error-correcting coding.InV.A.Andreev,A.V. Bourdine,V.A. Burdin,O. G. Morozov,&A. H. Sultanov (Eds.), Optical Technologies for Telecommunications 2018 (Vol. 11146, p. 111461A). Spie-Int Soc Optical Engineering. https://doi.org/10.1117/12.2526922 Trien,L.T.,&Yamao,Y.(2018).Informationdeliverydelayreductionbyrelay-assisted broadcast transmission for ITS V2V communications. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, E101A(9),1290–1297. https://doi.org/10.1587/transfun.E101.A.1290 Tseng,S.-P.,Lin,J.-Y., Cheng,W.-C.,Yeh, L.-Y.,& Shen,C.-Y. (2022).Decentralized federated learning with enhanced privacy preservation. 2022 IEEE International Conference on Multimedia and Expo Workshops. https://doi.org/10.1109/ICMEW56448.2022.9859507 Tuli, S., Ilager, S., Ramamohanarao, K., & Buyya, R. (2022). Dynamic scheduling for stochasticedge-cloudcomputingenvironments usingA3C learningandresidual recurrent neural networks. IEEE Transactions on Mobile Computing, 21(3), 940–954. https://doi.org/10.1109/TMC.2020.3017079 Uprety,A., Rawat, D. B., & Li,J. (2021). Privacy preservingmisbehavior detection in iov using federated machine learning. 2021 IEEE 18th Annual Consumer Communications & Networking Conference, 1–6. Uttley,L.,Quintana,D.S.,Montgomery,P.,Carroll,C.,Page,M.J.,Falzon,L.,Sutton, A., & Moher, D. (2023). The problems with systematic reviews: A living systematic review. Journal of Clinical Epidemiology, 156, 30–41. https://doi.org/10.1016/j.jclinepi.2023.01.011 Valizadeh,M.(2021).UsingGooglekeyboardinL2Writing:ImpactsonLexicalerrors reduction. Journal of Language Teaching and Learning, 11(2),61–80. Verbraeken,J.,Wolting,M.,Katzy,J.,Kloppenburg,J.,Verbelen,T.,&Rellermeyer,J. S. (2020).ASurvey on distributed machinelearning. ACM Computing Surveys, 53(2),30. https://doi.org/10.1145/3377454 Vladimirov, S. S., Karavaev, D.A., Stepanov,A. I., Yurchenko, M.A., & Vladyko,A. G. (2019).An application of LoRa technology for SD-IoVvetwork. 2019 11th International Congress on Ultra Modern Telecommunications And Control Systems And Workshops. https://doi.org/10.1109/icumt48472.2019.8970938 Wainakh,A., Guinea,A. S., Grube, T., & Muhlhauser, M. (2020). Enhancing privacy via hierarchical federated learning. 2020 IEEE European Symposium on Security and Privacy Workshops, 344–347. https://doi.org/10.1109/EuroSPW51379.2020.00053 Wang, F., Li, G., Wang, Y., Rafique, W., Khosravi, M. R., Liu, G., Liu, Y., & Qi, L. (2023). Privacy-aware traffic flow prediction based on multi-party sensor data with zero trust in smart city. ACM Transactions on Internet TECHNOLOGY, 23(3),44. https://doi.org/10.1145/3511904 Wang, F., Wei, W., & Liang, J. (2022). A group incremental approach for feature selection on hybrid data. Soft Computing, 26(8), 3663–3677. https://doi.org/10.1007/s00500-022-06838-x Wang, G., Xu, F., Zhang, H., & Zhao, C. (2022). Joint resource management for mobilitysupportedfederatedlearninginInternetof Vehicles.Future Generation Computer Systems, 129,199–211. https://doi.org/10.1016/j.future.2021.11.020 Wang, H., Wang, L., & Shen, J. (2022). Logit calibration for non-IID and long-tailed data in federated learning. 2022 IEEE Intl Conf on Parallel & Distributed Processing With Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking, 782–789. https://doi.org/10.1109/ISPA-BDCloud-SocialComSustainCom57177.2022.00105 Wang,J.,Cao,B.,Yu,P. S.,Sun,L.,Bao,W.,&Zhu,X. (2018).Deep learningtowards mobile applications. 2018 IEEE 38th International Conference on Distributed Computing Systems,1385–1393. https://doi.org/10.1109/ICDCS.2018.00139 Wang, J., Liu, Q., Liang, H., Joshi, G., & Poor, H. V. (2020). Tackling the objective inconsistency problem in heterogeneous federated optimization. Advances in Neural Information Processing Systems, 33,7611–7623. Wang,J.,Zhu,K.,&Hossain,E.(2022).GreenInternetof vehicles(IoV) inthe6Gera: Toward sustainable vehicular communications and networking. IEEE Transactions on Green Communications and Networking, 6(1), 391–423. https://doi.org/10.1109/TGCN.2021.3127923 Wang, K., Qi, X., & Liu, H. (2019). Photovoltaic power forecasting based LSTMconvolutional network. Energy, 189, 116225. https://doi.org/10.1016/j.energy.2019.116225 Wang, N., Chang, H., & Zhang, D. (2021). Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network. Computer Methods in Applied Mechanics and Engineering, 373, 113492. https://doi.org/10.1016/j.cma.2020.113492 Wang, P., He, R., Zhang, Q., Wang, J., Mihaylova, L., & Bouaynaya, N. C. (2020). Bayesian neural networks uncertainty quantification with cubature rules. 2020 International Joint Conference on Neural Networks. https://www.webofscience.com/wos/woscc/fullrecord/WOS:000626021404120 Wang,R., Li, Z.,Cao, J., Chen,T., &Wang,L. (2019).Convolutional recurrentneural networksfor textclassification. 2019 International Joint Conference on Neural Networks. https://www.webofscience.com/wos/woscc/summary/f500a5ac2871-4481-8269-0b7beb6da9cf-d13141d0/relevance/1 Wang, S., Liu, F., & Xia, H. (2021). Content-based vehicle selection and resource allocation for federated learning in IoV. 2021 IEEE Wireless Communications and Networking Conference Workshops. https://doi.org/10.1109/WCNCW49093.2021.9419986 Wang,X.,Che,M.,&Wei,Y. (2020).Tensor neuralnetworkmodelsfor tensor singular value decompositions. Computational Optimization and Applications, 75(3), 753–777. https://doi.org/10.1007/s10589-020-00167-1 Wang,X.,Fan,W.,Hu,X.,He,J.,&Chi,C.-H. (2023).Differentialprivacy-preserving of multi-party collaboration under federated learning in data center networks. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/TETCI.2023.3341299 Wang, X., Li, Y., Li, Y., Zhang, H., & Li, B. (2018). Collaborative filtering and data privacy protection: Overview and challenges. 2018 IEEE International Conference on Smart Cloud,218–223. Wang, X., Zhu, Y., Han, S., Yang, L., Gu, H., & Wang, F.-Y. (2022). Fast and progressive misbehavior detection in Internet of vehicles based on broad learning and incremental learning systems. IEEE Internet of Things Journal, 9(6),4788–4798. https://doi.org/10.1109/JIOT.2021.3109276 Wang,Y.,Xiong,L.,Niu,X.,Wang,Y.,&Liang,D.(2022).Afederatedlearningbased privacy-preservingdatasharing schemefor Internetof vehicles. In E.Ahene& F. Li (Eds.), Frontiers in Cyber Security, FCS 2022 (Vol. 1726, pp. 18–33). Springer International Publishing Ag. https://doi.org/10.1007/978-981-198445-7_2 Wang, Z., Yan, B., & Yao, Y. (2021). Blockchain empowered federated learning for medical data sharing model. In Z. Liu, F. Wu, & S. K. Das (Eds.), Wireless Algorithms, Systems, and Applications, Wasa 2021, Pt Iii (Vol. 12939,pp. 537– 544). Springer International PublishingAg. https://doi.org/10.1007/978-3-03086137-7_57 Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020). Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics And Security, 15, 3454–3469. https://doi.org/10.1109/TIFS.2020.2988575 Wright,S.A.(2019).PrivacyinIoTblockchains:Withbigdatacomesbigresponsibility. In C. Baru, J. Huan, L. Khan, X. H. Hu, R.Ak,Y. Tian, R. Barga, C. Zaniolo, K.Lee,&Y.F.Ye(Eds.),2019 IEEE International Conference on Big Data (pp. 5282–5291). IEEE. https://www.webofscience.com/wos/woscc/summary/2df1549b-7f9b-4b38b849-fc2bdfecdc8a-d1288fc0/relevance/1 Wu, B., Wang, D., Zhao, G., Deng, L., & Li, G. (2020). Hybrid tensor decomposition in neural network compression. Neural Networks, 132, 309–320. https://doi.org/10.1016/j.neunet.2020.09.006 Wu, D., Pan, M., Xu, Z., Zhang, Y., & Han, Z. (2020). Towards efficient secure aggregation for model update in federated learning. 2020 IEEE Global Communications Conference. https://doi.org/10.1109/GLOBECOM42002.2020.9347960 Wu, J., Chen, X., Zhang, L., Zhou, S., & Wang, D. (2022).A multi-secret reputation adjustment method in the secret sharing for internet of vehicles. Security and Communication Networks, 2022, 1413976. https://doi.org/10.1155/2022/1413976 Wu, L., Jin,Y., & Hao, K. (2023). Optimized compressed sensing for communication efficient federated learning. Knowledge-Based Systems, 278, 110805. https://doi.org/10.1016/j.knosys.2023.110805 Wu,Q.,Liu,L.,&Xue,S. (2022).Global updateguided federatedlearning.In Z. Li&J. Sun (Eds.), 2022 41st Chinese Control Conference (pp. 2434–2439). IEEE. https://www.webofscience.com/wos/woscc/summary/d2a9e4b4-d4b3-4a289d72-cd6db7732996-d130855f/relevance/1 Wu, T., Jiang, M., Han, Y., Yuan, Z., Li, X., & Zhang, L. (2021). A traffic-aware federated imitation learning framework for motion control at unsignalized intersections with internet of vehicles. Electronics, 10(24), 3050. https://doi.org/10.3390/electronics10243050 Xiao,J.,Du, C., Duan,Z.,&Guo,W. (2021). A novelserver-sideaggregationstrategy for federated learning in Non-IID situations. In R. Potolea, B. Iancu, & R. R. Slavescu (Eds.), 2021 20th International Symposium on Parallel and Distributed Computing (pp. 17–24). IEEE. https://doi.org/10.1109/ISPDC52870.2021.9521631 Xiao, W., Xue, J., Miao, Y., Li, Z., Chen, C., Wu, M., Li, W., & Zhou, L. (2020). Distributed graph computation meets machine learning. IEEE Transactions on Parallel and Distributed Systems, 31(7), 1588–1604. https://doi.org/10.1109/TPDS.2020.2970047 Xiaoqi,Q.,Xiaoqin,L.,Chao,M.,&Kang,M.(2021).Asummaryof researchprogress of singleimagetoremoverainandfog basedondeep learning. In J. Su,J. Chu, H. Jiang, & Q. Yu (Eds.), Seventh Symposium on Novel Photoelectronic Detection Technology and Applications (Vol. 11763,p. 117631T).Spie-Int Soc OpticalEngineering. https://doi.org/10.1117/12.2586305 Xie,Y.,Bhojwani,R.,Shekhar,S.,&Knight,J. (2018).An unsupervisedaugmentation framework for deep learning based geospatial object detection:Asummary of results. In F. BanaeiKashani, E. Hoel, R. H. Guting, R. Tamassia, & L. Xiong (Eds.), 26th ACM Sigspatial International Conference on Advances in Geographic Information Systems (ACM Sigspatial Gis 2018) (pp. 349–358). AssocComputing Machinery. https://doi.org/10.1145/3274895.3274901 Xu, C., Qu, Y., Luan, T. H. H., Eklund, P. W. W., Xiang, Y., & Gao, L. (2023). An efficient and reliable asynchronous federated learning scheme for smart public transportation. IEEE Transactions on Vehicular Technology, 72(5),6584–6598. https://doi.org/10.1109/TVT.2022.3232603 Xu, G., Li, H., Liu, S., Yang, K., & Lin, X. (2020). VerifyNet: Secure and verifiable federated learning. IEEE Transactions on Information Forensics and Security, 15,911–926. https://doi.org/10.1109/TIFS.2019.2929409 Xu, G., Li, H., Zhang,Y., Xu, S., Ning, J., & Deng, R. H. (2022). Privacy-Preserving federateddeeplearningwithirregular users.IEEE Transactions on Dependable And Secure Computing, 19(2), 1364–1381. https://doi.org/10.1109/TDSC.2020.3005909 Xu, H., Zhang, L., Li, P., & Zhu, F. (2022). Outlier detection algorithm based on k-nearest neighbors-local outlier factor. Journal of Algorithms & Computational Technology, 16,17483026221078111. Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learningfor healthcareinformatics.Journal of Healthcare Informatics Research, 5(1),1–19. https://doi.org/10.1007/s41666-020-00082-4 Xu, J., Yu, F. R., Wang, J., Qi, Q., Sun, H., & Liao, J. (2021). Capsule network distributed learning with multi-access edge computing for the Internet of vehicles. IEEE Communications Magazine, 59(8), 52–57. https://doi.org/10.1109/MCOM.001.2001130 Xu, L., Wang, H., & Gulliver, T. A. (2021). Outage probability performance analysis andpredictionfor mobileIoVnetworksbasedonICS-BPneuralnetwork.IEEE Internet of Things Journal, 8(5), 3524–3533. https://doi.org/10.1109/JIOT.2020.3023694 Xu,L.,Zhou,X.,Khan,M.A., Li,X.,Menon,V. G.,&Yu,X. (2022).Communication quality prediction for Internet of vehicle (IoV) networks:An Elman approach. IEEE Transactions on Intelligent Transportation Systems,23(10),19644–19654. https://doi.org/10.1109/TITS.2021.3088862 Xu, X., Fang, Z., Zhang, J., He, Q., Yu, D., Qi, L., & Dou, W. (2021). Edge content caching with deep spatiotemporal residual network for IoVin smart city. ACM Transactions on Sensor Networks, 17(3),29. https://doi.org/10.1145/3447032 Xu, X., Jiang, Q., Zhang, P., Cao, X., Khosravi, M. R.,Alex, L. T., Qi, L., & Dou, W. (2022). Game theory for distributed IoV Task offloading with fuzzy neural network in edge computing. IEEE Transactions on Fuzzy Systems, 30(11), 4593–4604. https://doi.org/10.1109/TFUZZ.2022.3158000 Xu,X.,Li,H.,Xu,W.,Liu,Z.,Yao,L.,&Dai,F.(2022).Artificialintelligencefor edge service optimization in Internet of vehicles: A survey. Tsinghua Science and Technology, 27(2),270–287. https://doi.org/10.26599/TST.2020.9010025 Xu, X., Liu, W., Zhang, Y., Zhang, X., Dou, W., Qi, L., & Bhuiyan, M. Z. A. (2022). PSDF:Privacy-awareIoVservicedeploymentwithfederatedlearningincloudedge computing. ACM Transactions on Intelligent Systems and Technology, 13(5),70. https://doi.org/10.1145/3501810 Xu,Z.,Liang,W.,Li,K.-C.,Xu,J.,&Jin,H. (2021).Ablockchain-basedroadsideunitassisted authentication and key agreement protocol for Internet of vehicles. Journal of Parallel and Distributed Computing, 149, 29–39. https://doi.org/10.1016/j.jpdc.2020.11.003 Yang, J., Zhang, Q., Liu, K., Jin, P., & Zhao, G. (2020). Federated learning in big data application and sharing. In A. J. TallonBallesteros (Ed.), Fuzzy Systems and Data Mining VI (Vol. 331, pp. 423–435). IOS Press. https://doi.org/10.3233/FAIA200721 Yang, P., Yan, M., Cui, Y., He, P., Wu, D., Wang, R., & Chen, L. (2022). FedDD: Federated double distillation in IoV. 2022 IEEE 96th Vehicular Technology Conference (VTC2022-FALL). https://doi.org/10.1109/VTC2022Fall57202.2022.10012798 Yang, P., Yan, M., Cui, Y., He, P., Wu, D., Wang, R., & Chen, L. (2023). Communication-efficient federated double distillation in IoV. IEEE Transactions on Cognitive Communications and Networking, 9(5),1340–1352. https://doi.org/10.1109/TCCN.2023.3286665 Yang,Q.,Gu,Y.,&Wu,D. (2019).Surveyof incrementallearning. Proceedings of the 2019 31st Chinese Control and Decision Conference (Ccdc 2019), 399–404. https://www.webofscience.com/wos/woscc/summary/02036375-6ee9-42a19769-59a445657af6-5445d248/relevance/1 Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2),1–19. https://doi.org/10.1145/3298981 Ye, D., Yu, R., Pan, M., & Han, Z. (2020). Federated learning in vehicular edge computing:A selective model aggregation approach. IEEE Access, 8, 23920– 23935. Yin,K.-L.,Pu,Y.-F.,&Lu,L.(2020).Hermitefunctionallinkartificial-neural-networkassisted adaptive algorithms for IoV nonlinear active noise control. IEEE Internet of Things Journal, 7(9), 8372–8383. https://doi.org/10.1109/JIOT.2020.2989761 Yin,X., Ma,L., Sun,P., &Tan,X. (2021).Avisualfingerprintupdatealgorithmbased on crowdsourced localizationand deeplearningfor smartIoV. Eurasip Journal on Advances In Signal Processing,2021(1),84.https://doi.org/10.1186/s13634021-00795-7 You,X.,Liu, X., Jiang,N., Cai,J.,&Ying,Z. (2023).Reschedule gradients:Temporal non-IID resilient federated learning. IEEE Internet of Things Journal, 10(1), 747–762. https://doi.org/10.1109/JIOT.2022.3203233 Yu,B., Mao,W., Lv,Y.,Zhang,C.,&Xie,Y. (2022).Asurveyonfederated learningin data mining. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 12(1),e1443. https://doi.org/10.1002/widm.1443 Yu,H., Liu, Z., Liu,Y., Chen,T., Cong, M., Weng,X., Niyato, D., &Yang, Q. (2020). A fairness-aware incentive scheme for federated learning. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society,393–399. Yu, H., Zhang, H.,Yu, X., Du, X., & Guizani, M. (2021). PGRide: Privacy-preserving group ridesharing matching in online ride hailing services. IEEE Internet of Things Journal, 8(7),5722–5735. https://doi.org/10.1109/JIOT.2020.3030274 Yu, Z., Cui,Y., Yu, J., Wang, M., Tao, D., & Tian, Q. (2020). Deep multimodal neural architecture search. Mm ’20: Proceedings of the 28th Acm International Conference on Multimedia, 3743–3752. https://doi.org/10.1145/3394171.3413977 Zaminpardaz, S., & Teunissen, P. J. G. (2017). Analysis of Galileo IOV plus FOC signals and E5 RTK performance. GPS Solutions, 21(4), 1855–1870. https://doi.org/10.1007/s10291-017-0659-9 Zavvos, E., Gerding, E. H.,Yazdanpanah, V., Maple, C., Stein, S., & Schraefel, M. C. (2022). Privacy and trust in the Internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(8), 10126–10141. https://doi.org/10.1109/TITS.2021.3121125 Zeng,S., Mi,B.,&Huang,D. (2023). Emergency vehicleidentificationfor Internetof vehicles based on federated learning and homomorphic encryption. In M. Sun &Z. Chen(Eds.), 2023 IEEE 12th Data Driven Control and Learning Systems Conference (pp. 208–213). IEEE. https://doi.org/10.1109/DDCLS58216.2023.10166254 Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775. https://doi.org/10.1016/j.knosys.2021.106775 Zhang, D., Shi, W., St-Hilaire, M., & Yang, R. (2022). Multiaccess edge integrated networking for Internet of vehicles: A blockchain-based deep compressed cooperativelearningapproach.IEEE Transactions on Intelligent Transportation Systems, 1–15. https://doi.org/10.1109/TITS.2022.3183927 Zhang, J., & Letaief, K. B. (2020). Mobile edge intelligence and computing for the Internet of vehicles. Proceedings of the IEEE, 108(2), 246–261. https://doi.org/10.1109/JPROC.2019.2947490 Zhang, J., Wang, C., & Li, S. (2024). Differential private knowledge trading in vehicular federated learning using contract theory. Knowledge-Based Systems, 285,111356. https://doi.org/10.1016/j.knosys.2023.111356 Zhang, J., Xin, Y., Wang, Y., Lei, X., & Yang, Y. (2023). A Secure energy Internet schemefor IoVbasedonpost-quantumblockchain.CMC-Computers Materials & Continua, 75(3),6323–6336. https://doi.org/10.32604/cmc.2023.034668 Zhang, S., Liu, X., Zhang, M., & Wo, T. (2017). PaIndex:An online index system for vehicle trajectory data exploiting parallelism. 2017 4th International Conference on Systems and Informatics, 696–703. https://www.webofscience.com/wos/woscc/summary/014928ec-7f74-44a49b51-2eb56cb85528-d12f4e95/relevance/1 Zhang,T.,Zhang,D.,Yan,H.,Qiu,J.,&Gao,J. (2021).Anewmethodof datamissing estimationwithFNN-basedtensor heterogeneousensemblelearningfor internet of vehicle. Neurocomputing, 420, 98–110. https://doi.org/10.1016/j.neucom.2020.09.042 Zhang, Y., Zhang, L., Wu, Q., & Mu, Y. (2022). Blockchain-enabled efficient distributedattribute-basedaccesscontrolframeworkwithprivacy-preservingin IoV. Journal of King Saud University-Computer and Information Sciences, 34(10),9216–9227. https://doi.org/10.1016/j.jksuci.2022.09.004 Zhang,Y.,Zhao,P.,Zhao,Y.,Yan,Z.,& Li, B.(2018). Themodelresearchon location fusion algorithm with big data selection and accuracy correction. DEStech Transactions on Computer Science and Engineering,(Wicom). Zhao,B.,Liu,X.,Chen,W.-N.,&Deng,R.H.(2023).CROWDFL:Privacy-preserving mobile crowdsensing system via federated learning. IEEE Transactions on Mobile Computing, 22(8), 4607–4619. https://doi.org/10.1109/TMC.2022.3157603 Zhao, F., Zhao, L., Wang, L., & Song, H. (2020). An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion. Expert Systems with Applications, 160, 113678. https://doi.org/10.1016/j.eswa.2020.113678 Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y. (2020).Localdifferentialprivacy-basedfederatedlearningfor internetof things. IEEE Internet of Things Journal, 8(11),8836–8853. Zheng,T.,Li,A.,Chen,Z.,Wang,H.,&Luo,J. (2023).AutoFed:Heterogeneity-aware federated multimodal learning for robust autonomous driving. Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, MOBICOM 2023, 209–223. https://doi.org/10.1145/3570361.3592517 Zhou,A.,Jiang,N., &Tang,T. (2024). Asynchronous robustaggregationmethodwith privacy protection for IoV federated learning. World Electric Vehicle Journal, 15(1),18. https://doi.org/10.3390/wevj15010018 Zhou, D.-X. (2020). Universality of deep convolutional neural networks. Applied and Computational Harmonic Analysis, 48(2), 787–794. https://doi.org/10.1016/j.acha.2019.06.004 Zhou,H.,Xu,W.,Chen,J.,&Wang,W.(2020).EvolutionaryV2Xtechnologiestoward theInternetof vehicles:Challengesandopportunities. Proceedings of the IEEE, 108(2),308–323. Zhou, Z., Gao, C., Xu, C., Zhang,Y., Mumtaz, S., & Rodriguez, J. (2018).Social bigdata-basedcontentdisseminationinInternetof vehicles. IEEE Transactions on Industrial Informatics, 14(2), 768–777. https://doi.org/10.1109/TII.2017.2733001 Zhou,Z.,Tian,Y.,&Peng,C.(2021).Privacy-preservingfederatedlearningframework with general aggregation and multiparty entity matching. Wireless Communications & Mobile Computing, 2021, 6692061. https://doi.org/10.1155/2021/6692061 Zhu, J., & Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321–335. https://doi.org/10.1007/s11192-020-03387-8 Zhu, M., Chen, Z., Chen, K., Lv, N., & Zhong, Y. (2022).Attention-based federated incrementallearningfor trafficclassificationintheInternetof things.Computer Communications, 185, 168–175. https://doi.org/10.1016/j.comcom.2022.01.006 Zhu, X., Wang, J., Hong, Z., Xia, T., & Xiao, J. (2019). Federated learning of unsegmented Chinese text recognition model. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence, 1341–1345. https://doi.org/10.1109/ICTAI.2019.00186 Zhuang,W.,Ye,Q.,Lyu,F.,Cheng,N.,&Ren,J.(2020).SDN/NFV-empoweredfuture IoV With enhanced communication, computing, and caching. Proceedings of the IEEE, 108(2),274–291. https://doi.org/10.1109/JPROC.2019.2951169 Zou, S., Xiao, M., Xu, Y., An, B., & Zheng, J. (2021). FedDCS: Federated Learning framework based on dynamic client selection. 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, 627–632. https://doi.org/10.1109/MASS52906.2021.00090 Zucchetta, P., Branchini, M., Zorz,A., Bodanza, V., Cecchin, D., Paiusco, M., & Bui, F. (2019).Quantitativeanalysisof imagemetricsfor reduced andstandarddose pediatric 18F-FDG PET/MRI examinations. British Journal of Radiology, 92(1095),20180438. https://doi.org/10.1259/bjr.20180438
|
This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |