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Federated Aggregation Scheme With Authentication for Securing IoT-Enabled Social Internet of Vehicles: Enhancing Scalability and Loss Optimization
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DOI：10.1109/ojvt.2026.3695770.png)
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Abstract
En 中文
In the Social Internet of Vehicles (SIoV) based on Digital Twin (DT), which incorporates the social networking concepts from the Social Internet of Things (SIoT), Federated Learning (FL) enables distributed vehicular clients to collaboratively learn machine learning models while preserving data privacy and minimizing communication overhead. However, susceptible model parameters render FL vulnerable to inference and impersonation attacks, necessitating strong and lightweight authentication protocols. This paper introduces a federated learning–based collaborative authentication protocol that incorporates anonymous mutual authentication and dynamic key agreement between vehicles and infrastructure nodes, enabling privacy-preserving data sharing while eliminating reliance on centralized entities. The protocol is evaluated using three aggregation strategies: FedAvg, FedProx, and FedOpt under varying client participation levels and epoch configurations. Experimental results demonstrate that FedOpt achieves superior convergence and generalization, reducing average loss by approximately 7.0% compared to FedAvg and 6.5% compared to FedProx, particularly in heterogeneous environments. Security analysis under the stochastic predictive machine model further validates robustness against adversarial threats. Overall, the proposed protocol enhances scalability, reduces propagation delay, and ensures efficient, privacy-preserving authentication in dynamic SIoV networks.
Keywords:
Internet of Things (IoT)
federated learning
Social Internet of Vehicles (SIoV)
collaborative authentication
FedOpt
privacy-preserving communication
loss optimization
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