{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T05:43:48Z","timestamp":1775281428233,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T00:00:00Z","timestamp":1752883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>The Internet of Vehicles (IoV) presents a vast opportunity for optimised traffic flow, road safety, and enhanced usage experience with the influence of Federated Learning (FL). However, the distributed nature of IoV networks creates certain inherent problems regarding data privacy, security from adversarial attacks, and the handling of available resources. This paper introduces Fed-DTB, a new dynamic trust-based framework for FL that aims to overcome these challenges in the context of IoV. Fed-DTB integrates the adaptive trust evaluation that is capable of quickly identifying and excluding malicious clients to maintain the authenticity of the learning process. A performance comparison with previous approaches is shown, where the Fed-DTB method improves accuracy in the first two training rounds and decreases the per-round training time. The Fed-DTB is robust to non-IID data distributions and outperforms all other state-of-the-art approaches regarding the final accuracy (87\u201388%), convergence rate, and adversary detection (99.86% accuracy). The key contributions include (1) a multi-factor trust evaluation mechanism with seven contextual factors, (2) correlation-based adaptive weighting that dynamically prioritises trust factors based on vehicular conditions, and (3) an optimisation-based client selection strategy that maximises collaborative reliability. This work opens up opportunities for more accurate, secure, and private collaborative learning in future intelligent transportation systems with the help of federated learning while overcoming the conventional trade-off of security vs. efficiency.<\/jats:p>","DOI":"10.3390\/jcp5030048","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T09:33:53Z","timestamp":1753090433000},"page":"48","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fed-DTB: A Dynamic Trust-Based Framework for Secure and Efficient Federated Learning in IoV Networks: Securing V2V\/V2I Communication"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0310-6516","authenticated-orcid":false,"given":"Ahmed","family":"Alruwaili","sequence":"first","affiliation":[{"name":"Institute for Sustainable Industries & Liveable Cities (ISILC), Victoria University, Melbourne, VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9451-7390","authenticated-orcid":false,"given":"Sardar","family":"Islam","sequence":"additional","affiliation":[{"name":"Institute for Sustainable Industries & Liveable Cities (ISILC), Victoria University, Melbourne, VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7963-2446","authenticated-orcid":false,"given":"Iqbal","family":"Gondal","sequence":"additional","affiliation":[{"name":"School of Computing Tech, Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC 3000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,19]]},"reference":[{"key":"ref_1","unstructured":"Zhou, J., Zhang, S., Lu, Q., Dai, W., Chen, M., Liu, X., Pirttikangas, S., Shi, Y., Zhang, W., and Herrera-Viedma, E. (2021). A survey on federated learning and its applications for accelerating industrial internet of things. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A.A.A., Yogamani, S., and P\u00e9rez, P. (2021). Deep Reinforcement Learning for Autonomous Driving: A Survey. arXiv.","DOI":"10.1109\/TITS.2021.3054625"},{"key":"ref_3","first-page":"18","article-title":"Federated reinforcement learning: Techniques, applications, and open challenges","volume":"1","author":"Qi","year":"2021","journal-title":"Intell. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Malik, S., Khan, M.A., and El-Sayed, H. (2021). Collaborative Autonomous Driving\u2014A Survey of Solution Approaches and Future Challenges. Sensors, 21.","DOI":"10.3390\/s21113783"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3347","DOI":"10.1109\/TKDE.2021.3124599","article-title":"A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.3390\/smartcities7040082","article-title":"Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series","volume":"7","author":"Richter","year":"2024","journal-title":"Smart Cities"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Chen, T., and Tong, Y. (2019). Federated Machine Learning: Concept and Applications. arXiv.","DOI":"10.1145\/3298981"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s42400-021-00077-7","article-title":"A critical review of intrusion detection systems in the internet of things: Techniques, deployment strategy, validation strategy, attacks, public datasets and challenges","volume":"4","author":"Khraisat","year":"2021","journal-title":"Cybersecurity"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450288","article-title":"A Systematic Literature Review on Federated Machine Learning: From a Software Engineering Perspective","volume":"54","author":"Lo","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12248","DOI":"10.1109\/JIOT.2023.3245721","article-title":"Multiagent Reinforcement Learning-Based Cooperative Multitype Task Offloading Strategy for Internet of Vehicles in B5G\/6G Network","volume":"10","author":"Cui","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.63180\/jcsra.thestap.2025.3.10","article-title":"Utilizing IDS and IPS to Improve Cybersecurity Monitoring Process","volume":"2025","author":"Ang","year":"2025","journal-title":"J. Cyber Secur. Risk Audit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3975","DOI":"10.1109\/TITS.2020.3002712","article-title":"A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in Internet of Vehicles","volume":"22","author":"Chai","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.dcan.2022.05.020","article-title":"A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles","volume":"10","author":"Wang","year":"2024","journal-title":"Digit. Commun. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"175744","DOI":"10.1109\/ACCESS.2019.2956955","article-title":"Proof-of-Reputation Based-Consortium Blockchain for Trust Resource Sharing in Internet of Vehicles","volume":"7","author":"Chai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qi, J.-J., and Li, Z.-Z. (2005, January 15\u201317). Managing Trust for Secure Active Networks. Proceedings of the Multi-Agent Systems and Applications IV, Budapest, Hungary.","DOI":"10.1007\/11559221_77"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aiche, A., Tardif, P.-M., and Erritali, M. (2024). Modeling Trust in IoT Systems for Drinking-Water Management. Future Internet, 16.","DOI":"10.3390\/fi16080273"},{"key":"ref_17","first-page":"99","article-title":"Implementation of a Trust-Based Framework for Substation Defense in the Smart Grid","volume":"7","author":"Ghorbani","year":"2024","journal-title":"Smart Cities"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1007\/s10796-022-10307-z","article-title":"Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection","volume":"26","author":"Rjoub","year":"2022","journal-title":"Inf. Syst. Front."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nishio, T., and Yonetani, R. (2019, January 20\u201324). Client selection for federated learning with heterogeneous resources in mobile edge. Proceedings of the ICC 2019\u20142019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref_20","unstructured":"Mazloomi, F., Heydari, S.S., and El-Khatib, K. (November, January 30). Trust-based Knowledge Sharing Among Federated Learning Servers in Vehicular Edge Computing. Proceedings of the Int\u2019l ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, Montreal, QC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1109\/COMST.2021.3075439","article-title":"Federated learning for internet of things: A comprehensive survey","volume":"23","author":"Nguyen","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cao, J., Zhang, K., Wu, F., and Leng, S. (2020, January 25\u201328). Learning cooperation schemes for mobile edge computing empowered Internet of Vehicles. Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Republic of Korea.","DOI":"10.1109\/WCNC45663.2020.9120493"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MNET.011.1900317","article-title":"Federated learning for data privacy preservation in vehicular cyber-physical systems","volume":"34","author":"Lu","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8836","DOI":"10.1109\/JIOT.2020.3037194","article-title":"Local differential privacy-based federated learning for internet of things","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bugliesi, M., Preneel, B., Sassone, V., and Wegener, I. (2006). Differential Privacy. Automata, Languages and Programming, Springer.","DOI":"10.1007\/11786986"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6380","DOI":"10.1109\/JIOT.2019.2962715","article-title":"Deep-reinforcement-learning based mode selection and resource allocation for cellular V2X communications","volume":"7","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_27","first-page":"8050","article-title":"Privacypreserved task offloading in mobile blockchain with deep reinforcement learning","volume":"68","author":"Nguyen","year":"2019","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bai, J., and Dong, H. (2023, January 2\u20138). Federated Learning-driven Trust Prediction for Mobile Edge Computing-based IoT Systems. Proceedings of the 2023 IEEE International Conference on Web Services (ICWS), Chicago, IL, USA.","DOI":"10.1109\/ICWS60048.2023.00031"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Albaseer, A., Ciftler, B.S., Abdallah, M., and Al-Fuqaha, A. (2020, January 15\u201319). Exploiting unlabeled data in smart cities using federated edge learning. Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus.","DOI":"10.1109\/IWCMC48107.2020.9148475"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.dss.2005.05.019","article-title":"A survey of trust and reputation systems for online service provision","volume":"43","author":"Ismail","year":"2007","journal-title":"Decis. Support Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"36","DOI":"10.63180\/jcsra.thestap.2025.1.4","article-title":"Machine Learning for Cybersecurity Issues: A systematic Review","volume":"2025","author":"Alshuaibi","year":"2025","journal-title":"J. Cyber Secur. Risk Audit."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/JIOT.2022.3201231","article-title":"Practical Private Aggregation in Federated Learning Against Inference Attack","volume":"10","author":"Zhao","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/3630099","article-title":"FedSuper: A Byzantine-Robust Federated Learning Under Supervision","volume":"20","author":"Zhao","year":"2024","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"23","DOI":"10.63180\/jcsra.thestap.2025.3.3","article-title":"Security and Privacy Challenges and Solutions in Autonomous Driving Systems: A Comprehensive Review","volume":"2025","author":"Lippi","year":"2025","journal-title":"J. Cyber Secur. Risk Audit."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Alruwaili, A., Islam, S.M.N., and Gondal, I. (2025). Cybersecurity in Robotic Autonomous Vehicles: Machine Learning Applications to Detect Cyber Attacks, CRC Press. [1st ed.].","DOI":"10.1201\/9781003610908"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1109\/JPROC.2011.2132790","article-title":"Dedicated Short-Range Communications (DSRC) Standards in the United States","volume":"99","author":"Kenney","year":"2011","journal-title":"Proc. IEEE"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3501","DOI":"10.1109\/TII.2021.3119038","article-title":"Federated learning for cybersecurity: Concepts, challenges and future directions","volume":"18","author":"Alazab","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"47","DOI":"10.63180\/jcsra.thestap.2025.1.5","article-title":"Assessment of cybersecurity threats and defense mechanisms in wireless sensor networks","volume":"2025","author":"Alotaibi","year":"2025","journal-title":"J. Cyber Secur. Risk Audit."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.63180\/jcsra.thestap.2025.1.1","article-title":"Cybersecurity threats, countermeasures and mitigation techniques on the IoT: Future research directions","volume":"1","author":"Almuqren","year":"2025","journal-title":"J. Cyber Secur. Risk Audit."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gmiden, M., Gmiden, M.H., and Trabelsi, H. (2016, January 19\u201321). An intrusion detection method for securing in-vehicle CAN bus. Proceedings of the 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Sousse, Tunisia.","DOI":"10.1109\/STA.2016.7952095"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1186\/s13638-019-1484-3","article-title":"Intrusion detection system for automotive Controller Area Network (CAN) bus system: A review","volume":"2019","author":"Lokman","year":"2019","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_42","unstructured":"Daemen, J., and Rijmen, V. (2024, July 20). AES Proposal: Rijndael. Available online: https:\/\/www.cs.miami.edu\/home\/burt\/learning\/Csc688.012\/rijndael\/rijndael_doc_V2.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"N, J., and Patil, R. (2023, January 5\u20137). A Multi-tier accredit based security for trustworthiness in VANET\u2019s using broadcasting mechanism. Proceedings of the 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichirappalli, India.","DOI":"10.1109\/ICEEICT56924.2023.10157824"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, M., and Cui, S. (2024). Communication Efficient Federated Learning for Wireless Networks. Wireless Networks, Springer Nature.","DOI":"10.1007\/978-3-031-51266-7"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., and Yu, H. (2020). Federated Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, Springer International Publishing.","DOI":"10.1007\/978-3-031-01585-4"},{"key":"ref_46","unstructured":"(2021). Road Vehicles\u2014Cybersecurity Engineering. Beyond Security (Standard No. ISO\/SAE 21434:2021). Available online: https:\/\/www.iso.org\/standard\/70918.html."},{"key":"ref_47","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A.Y. (2017, January 20\u201322). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (PMLR), Fort Lauderdale, FL, USA. Available online: https:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html."},{"key":"ref_48","unstructured":"Krizhevsky, A., and Hinton, G. (2025, May 11). Learning Multiple Layers of Features from Tiny Images. Available online: https:\/\/www.cs.utoronto.ca\/~kriz\/learning-features-2009-TR.pdf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"9225","DOI":"10.1109\/TVT.2022.3176243","article-title":"RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern","volume":"71","author":"Kong","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, Y., Mahmood, A., Sabri, M.F.M., and Zen, H. (2024). TM\u2013IoV: A First-of-Its-Kind Multilabeled Trust Parameter Dataset for Evaluating Trust in the Internet of Vehicles. Data, 9.","DOI":"10.3390\/data9090103"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"22563","DOI":"10.1109\/TITS.2021.3095015","article-title":"Performance Evaluation for Secure Communications in Mobile Internet of Vehicles with Joint Reactive Jamming and Eavesdropping Attacks","volume":"23","author":"Bajracharya","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wan, J., Liu, J., Shao, Z., Vasilakos, A.V., Imran, M., and Zhou, K. (2016). Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles. Sensors, 16.","DOI":"10.3390\/s16010088"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"11365","DOI":"10.1109\/JIOT.2021.3128646","article-title":"Data poisoning attacks on federated machine learning","volume":"9","author":"Sun","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Deressa, B., and Hasan, M.A. (2024, January 20). TrustBandit: Optimizing Client Selection for Robust Federated Learning Against Poisoning Attacks. Proceedings of the IEEE INFOCOM 2024\u2014IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver, BC, Canada.","DOI":"10.1109\/INFOCOMWKSHPS61880.2024.10620802"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Awan, S., Luo, B., and Li, F. (2021, January 4\u20138). Contra: Defending against poisoning attacks in federated learning. Proceedings of the Computer Security\u2013ESORICS 2021: 26th European Symposium on Research in Computer Security, Darmstadt, Germany. Part I 26.","DOI":"10.1007\/978-3-030-88418-5_22"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Rizve, M.N., Khan, S., Khan, F.S., and Shah, M. (2021, January 20\u201325). Exploring complementary strengths of invariant and equivariant representations for few-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01069"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chang, S., and Liu, Y. (2023, January 10\u201312). FedCS: Communication-Efficient Federated Learning with Compressive Sensing. Proceedings of the 2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS), Nanjing, China.","DOI":"10.1109\/ICPADS56603.2022.00011"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5539","DOI":"10.1109\/TITS.2023.3336823","article-title":"RCFL: Redundancy-Aware Collaborative Federated Learning in Vehicular Networks","volume":"25","author":"Hui","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bai, Y., and Fan, M. (2021, January 23\u201326). A method to improve the privacy and security for federated learning. Proceedings of the 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), Chengdu, China.","DOI":"10.1109\/ICCCS52626.2021.9449214"}],"container-title":["Journal of Cybersecurity and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2624-800X\/5\/3\/48\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:12:45Z","timestamp":1760033565000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2624-800X\/5\/3\/48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,19]]},"references-count":59,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["jcp5030048"],"URL":"https:\/\/doi.org\/10.3390\/jcp5030048","relation":{},"ISSN":["2624-800X"],"issn-type":[{"value":"2624-800X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,19]]}}}