Trust-based Knowledge Sharing Among Federated Learning Servers in Vehicular Edge Computing
Fateme Mazloomi, Shahram Shah Heydari, Khalil El‐Khatib · 2023
Federated Learning (FL) protects privacy during autonomous vehicle machine learning (ML) operations. FL enables cooperative training of a single ML model across multiple edge devices, leveraging distributed datasets while maintaining data locality. Although much research has concentrated on single-server FL for autonomous driving applications within vehicular networks, real-world scenarios often involve several concurrent servers capable of benefiting from each other's knowledge. However, these servers' trustworthiness is paramount when using their global models, as an imprudent choice could significantly decrease FL performance and accuracy. In this paper, we introduce a novel trust-based knowledge-sharing approach among FL servers, wherein the accuracy of shared global models on clients' local data serves as the trust metric. Our proposed methodology enables servers to utilize shared global models from reliable servers for their clients, thereby improving training accuracy and reducing loss. This enhancement is particularly notable during the initial training rounds compared to base FL implementation.