{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:46:22Z","timestamp":1790833582168,"version":"4.1.0"},"reference-count":70,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>\n                    The Internet of Vehicles (IoV) supports essential intelligent transportation applications but encounters challenges in federated learning (FL) due to non-independent and identically distributed (non-IID) data, vehicle mobility, resource heterogeneity, and strict privacy requirements in latency-sensitive scenarios such as misbehavior detection and accident response. Traditional FL methods, such as random client selection and standard FedAvg, often experience slow convergence and reduced performance under non-IID conditions. We introduce a hierarchical federated learning framework for software-defined vehicular fog computing. The framework incorporates FedNova (a normalized-averaging aggregation method for heterogeneous federated optimization) to produce normalized model updates under data heterogeneity, a Reward-Based Payoff Strategy (RBPS) for incentive-aware client selection, and game-theoretic vehicle-aggregator matching based on the college admissions problem. Privacy is strengthened through quantum key distribution (QKD)-assisted secure key establishment and classical gradient masking, with quantum circuit simulation used to assess future enhancements. The three-layer architecture includes vehicles, Roadside Unit (RSU)\/ Base Station (BS)-level aggregators, and a Software-Defined Network Controller (SDNC) global aggregator. The framework uses both monetary and service-based incentives, such as toll exemptions, to encourage vehicle participation. Hybrid simulations using OMNeT++, Veins, SUMO, and the VeReMi misbehavior detection dataset show that the proposed approach achieves 94.8% classification accuracy [95% Confidence Interval (CI): 92.7\u201397.0 over 10 runs], converges in 120 rounds (33% faster than FedAvg), and reduces average latency by 29% (320 ms compared to 450 ms for FedAvg), with statistically significant improvements (\n                    <jats:italic>p<\/jats:italic>\n                    &amp;lt; 0.05). These gains enable faster model adaptation to evolving attacks (5\u201310 min shorter training cycles) and support real-time safety applications where delays above 400 ms can compromise road safety. Ablation studies confirm the complementary roles of FedNova, RBPS, and matching. Although quantum operations are currently simulated classically, the design remains compatible with future quantum hardware.\n                  <\/jats:p>","DOI":"10.3389\/frai.2026.1861374","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:04:06Z","timestamp":1785305046000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A hierarchical federated learning framework with FedNova, game-theoretic matching, and QKD-assisted privacy for the internet of vehicles"],"prefix":"10.3389","volume":"9","author":[{"given":"L.","family":"Jai Vinita","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V.","family":"Vetriselvi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, Anna University","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","article-title":"A survey on federated learning: the journey from centralized to distributed on-site learning and beyond","volume":"8","author":"Abdulrahman","year":"","journal-title":"IEEE Internet Things J"},{"key":"B2","doi-asserted-by":"publisher","first-page":"4723","DOI":"10.1109\/JIOT.2020.3028742","article-title":"Fedmccs: multicriteria client selection model for optimal iot federated learning","volume":"8","author":"AbdulRahman","year":"","journal-title":"IEEE Internet Things J"},{"key":"B3","article-title":"\u201cFederated learning based on dynamic regularization,\u201d","author":"Acar","year":"2021","journal-title":"Proceedings of the international conference on learning representations (ICLR)"},{"key":"B4","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/GLOBECOM48099.2022.10001569","article-title":"\u201cGreen federated learning via energy-aware client selection,\u201d","author":"Albelaihi","year":"2022","journal-title":"Proceedings of the IEEE global communications conference (GLOBECOM)"},{"key":"B5","first-page":"678","article-title":"AI-fl: an intelligent federated learning framework for cyberthreat detection in smart cities with enhanced privacy","volume":"26","author":"Alghamdi","year":"2025","journal-title":"IEEE Trans. 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