[1] X. Li et al., "Federated Learning for Autonomous Driving: Challenges and Solutions," IEEE Transactionson Intelligent Transportation Systems, vol. 25, no. 1, pp. 123–135, Jan. 2024.
[2] Y. Zhang and L. Wang, "Secure Aggregation in Federated Learning: A VANET Perspective," IEEEInternet of Things Journal, vol. 10, no. 5, pp. 4241–4250, Mar. 2023.
[3] J. Yang et al., "Privacy-preserving Collaborative Learning in Edge-VANETs," IEEE Transactions onVehicular Technology, vol. 72, no. 4, pp. 3972–3986, Apr. 2023.
[4] C. Xu et al., "EdgeFL: Federated Learning for Roadside-based Vehicular Security," IEEE Transactionson Network and Service Management, vol. 21, no. 1, pp. 88–102, Jan. 2024.
[5] H. B. McMahan et al., "Communication-efficient Learning of Deep Networks from Decentralized Data,"Proc. of the 20th Int. Conf. on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP, vol. 54, pp. 1273–1282, 2017.
[6] H. Wang et al., "FL-VANET: A Federated Learning-based VANET Security Architecture," IEEE Access,vol. 12, pp. 19872–19883, Feb. 2024.
[7] R. Zhou and K. Liu, "Privacy-preserving Intrusion Detection for VANETs Using Federated TransferLearning," IEEE Transactions on Information Forensics and Security, vol. 18, pp. 510–522, 2023.
[8] A. A. Alkhatib et al., "Smart Traffic Scheduling for Crowded Cities Road Networks," EgyptianInformatics Journal, vol. 23, no. 4, pp. 163–176, 2022.
[9] N. A. Al-Madi and A. A. Hnaif, "Optimizing Traffic Signals in Smart Cities Based on GeneticAlgorithm," Computer Sys. Science & Eng., vol. 40, no. 1, DOI:10.32604/csse.2022.016730, 2022.
[10] M. Rahman et al., "TrustFL: Trust-aware Federated Learning for Adversarial VANETs," IEEETransactions on Dependable and Secure Computing, Early Access, Dec. 2023.
[11] S. Ahmed et al., "VeriFL: Blockchain-enabled Federated Learning for Trustworthy VANET IntrusionDetection," IEEE Transactions on Intelligent Vehicles, vol. 8, no. 3, pp. 3121–3134, 2023.
[12] A. Elhabti et al., "Security in VANETs: A Review of Emerging Threats and FL Solutions," IEEECommunications Surveys & Tutorials, vol. 25, no. 4, pp. 3112–3133, 2023.
[13] A. Hassan and M. S. Khan, "Lightweight CNN-based Anomaly Detection in VANETs Using EdgeLearning," IEEE Access, vol. 12, pp. 45789–45798, 2024.
[14] K. Zhang et al., "Secure Federated Learning for Edge Intelligence in Vehicular Networks," IEEE Transactions on Mobile Computing, Early Access, 2024.
[15] H. Liu et al., "Cybersecurity Issues in Future VANETs: Challenges and Trends," IEEE CommunicationsSurveys & Tutorials, vol. 25, no. 2, pp. 1890–1911, 2023.
[16] F. Saleh et al., "FedMis: Federated Misbehavior Detection in VANETs Using GNNs," IEEE Internet ofThings Journal, vol. 10, no. 4, pp. 3792–3801, 2023.
[17] S. Bhat and K. Singh, "Blockchain-enhanced Federated Learning for VANET Security," IEEE Access,vol. 11, pp. 91123–91135, 2023.
[18] M. Qiu et al., "LightIDS: Lightweight Deep IDS for VANET Using Federated Transfer Learning," IEEETransactions on Vehicular Technology, vol. 73, no. 1, pp. 120–131, 2024.
[19] R. Patel and Y. Zhao, "Efficient Model Compression in Federated IDS for VANETs," IEEE Transactionson Mobile Computing, Early Access, 2025.
[20] L. Gao et al., "DP-FedVANET: Differential Privacy-preserving Federated IDS for VANET," IEEETransactions on Dependable and Secure Computing, vol. 20, no. 2, pp. 302–312, 2023.
[21] Y. Zheng et al., "Asynchronous Federated Learning for Fast Adversarial Defense in VANETs," IEEETransactions on Intelligent Vehicles, vol. 8, no. 4, pp. 4440–4452, 2023.
[22] M. Hasan et al., "VerifiD: Verifiable Federated IDS Using Homomorphic Encryption for VANETs,"IEEE Internet of Things Journal, vol. 11, no. 1, pp. 500–510, 2024.
[23] H. Deng and J. Xiao, "Federated Adversarial Training for VANET Security Systems," IEEE Transactionson Information Forensics and Security, Early Access, 2025.
[24] N. Raman et al., "Resilient Aggregation in Federated IDS for Urban Vehicular Networks," IEEE Access,vol. 12, pp. 78132–78145, 2024.
[25] Y. Kim et al., "TrustBlock: Trust and Blockchain-integrated Federated IDS for VANETs," IEEETransactions on Intelligent Transportation Systems, vol. 25, no. 3, pp. 2911–2923, 2024.
[26] H. Wu et al., "Handling Non-IID Data in FL-based VANET Intrusion Detection," IEEE CommunicationsLetters, vol. 27, no. 8, pp. 1891–1895, 2023.
[27] F. Tariq and B. Niazi, "Evaluation of Edge Aggregation Strategies in FL-based IDS for VANETs," IEEETransactions on Network and Service Management, vol. 20, no. 3, pp. 2078–2089, 2023.
[28] S. Yousef and A. Darwish, "Adaptive Client Participation in Energy-constrained FL for VANETs," IEEETransactions on Green Communications and Networking, Early Access, 2025.
[29] A. Mohammed et al., "Model Quantization for Energy-efficient FL in Vehicular IDS," IEEE Transactionson Sustainable Computing, vol. 9, no. 2, pp. 155–167, 2024.
[30] R. Alshammari et al., "Forensic Logging and Privacy Auditing in Federated VANET Security," IEEETransactions on Services Computing, vol. 16, no. 1, pp. 344–357, 2023.
[31] J. Li et al., "ReconFL: Reconstructing Gradient Attacks in FL for Vehicular IDS," IEEE Transactions onInformation Forensics and Security, vol. 19, pp. 450–463, 2024.
[32] L. Guo and P. Liu, "StreamIDS: Real-time Intrusion Detection in VANET via Federated OnlineLearning," IEEE Transactions on Mobile Computing, Early Access, 2025.
[33] Z. Rajab et al., "SecureCAM: Federated VANET Misbehavior Detection in Cooperative Messages," IEEETransactions on Vehicular Technology, vol. 72, no. 6, pp. 5433–5444, 2023.
[34] M. Hussain and Y. Fang, "Federated Learning Analytics for Large-scale VANET Intrusion Detection,"IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 101–114, 2024.
[35] A. Kalra and R. Singh, "A Survey on Federated Learning for IoT and VANET Security Applications,"IEEE Communications Surveys & Tutorials, vol. 25, no. 3, pp. 3304–3328, 2023.
[36] Y. Duan et al., "Adaptive Local Training for Efficient Federated Learning in VANETs," IEEETransactions on Mobile Computing, vol. 22, no. 4, pp. 3721–3734, 2023.
[37] M. Sharif et al., "Dynamic Aggregation in Privacy-aware Federated Learning for VANET IntrusionDetection," IEEE Access, vol. 12, pp. 101293–101308, 2024.
[38] Z. Tan et al., "Collaborative Defense in VANETs via Federated Adversarial Learning," IEEETransactions on Vehicular Technology, vol. 73, no. 2, pp. 1294–1307, 2024.
[39] N. Sharma and P. Kumar, "Privacy-preserving Distributed Intrusion Detection in FL-enabled VANETs,"IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4288–4299, 2023.
[40] X. Tian et al., "GraphFL-VANET: Graph Neural Networks and Federated Learning for Secure Routingin VANETs," IEEE Trans. on Network Science and Engineering, Early Access, 2024.
[41] T. Joseph et al., "Verifiable Federated Learning with Proof of Trust for VANET Security," IEEETransactions on Dependable and Secure Computing, Early Access, 2024.
[42] Y. Wang and M. Hu, "Differential Privacy in Cross-Silo Federated Learning for Vehicular AnomalyDetection," IEEE Transactions on Information Forensics and Security, vol. 19, pp. 512–524, 2024.
[43] H. Rao et al., "Robust Federated Learning against Malicious Updates in VANETs," IEEE Transactionson Network and Service Management, vol. 20, no. 4, pp. 3101–3113, 2023.
[44] S. Singh et al., "Mobility-aware Client Scheduling in FL for Urban VANET Environments," IEEETransactions on Intelligent Transportation Systems, vol. 24, no. 6, pp. 5813–5824, 2023.
[45] S. Alghamdi and T. Alasmary, "FL-VANET++: Multi-region Aggregation for Highway VANET Security," IEEE Transactions on Vehicular Technology, Early Access, 2025.
[46] J. Ma et al., "Incentive-aware Federated Learning for Misbehavior Detection in VANETs," IEEETransactions on Mobile Computing, vol. 23, no. 1, pp. 399–411, Jan. 2024.
[47] M. Karim and R. Yadav, "Blockchain-backed FL for Scalable Intrusion Detection in VANETs," IEEEInternet of Things Journal, vol. 11, no. 3, pp. 1783–1794, Mar. 2024.
[48] L. Sun et al., "Trust-aware Model Fusion in Federated VANETs for Intrusion Detection," IEEETransactions on Vehicular Technology, vol. 73, no. 1, pp. 951–962, Jan. 2024.
[49] C. Xu and Y. Lu, "Self-learning Federated IDS for VANETs under Limited Supervision," IEEETransactions on Artificial Intelligence, Early Access, 2025.
[50] S. Ghosh et al., "DeepChainFL: Blockchain and Deep Federated Learning for VANET IntrusionDetection," IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1812–1824, June 2023.
[51] F. Alqahtani and M. Zubair, "Fast Adaptive Federated Learning for Emergency Vehicle Routing inVANETs," IEEE Trans. on Intelligent Transportation Systems, vol. 25, no. 1, pp. 922–934, 2024.
[52] Q. Chen et al., "Resilient FL Aggregation for VANET Security under Byzantine Attacks," IEEETransactions on Dependable and Secure Computing, Early Access, 2025.
[53] P. Shukla and V. Nair, "Secure Model Update Mechanisms for FL in VANET IDS," IEEE Transactionson Information Forensics and Security, vol. 19, pp. 778–791, 2024.
[54] X. Zhou and L. Jiang, "Real-world FL Evaluation for VANET Threat Detection: Datasets andBenchmarks," IEEE Access, vol. 12, pp. 83271–83285, 2024.