Federated learning (FL) is a promising technology for autonomous driving, enabling connected and autonomous vehicles (CAVs) to collaborate in decision-making and environmental perception while preserving privacy. However, traditional FL algorithms face challenges related to imbalanced data distribution, fluctuating channel conditions, and potential security risks associated with malicious attacks on local models. This paper proposes a fair and secure FL algorithm that not only addresses the challenges arising from imbalanced data distribution and fluctuating channel conditions, but defends against malicious attacks. Specifically, we first propose a personalized local training round allocation algorithm to balance energy costs and accelerate model convergence. Next, in order to further guarantee security, we embed an attack module based on Gini impurity. Extensive simulations demonstrate that the proposed algorithm achieves energy fairness, reduces global iteration time, and exhibits resistance against malicious attacks.
A Secure Personalized Federated Learning Algorithm for Autonomous Driving
IEEE Transactions on Intelligent Transportation Systems ; 25 , 12 ; 20378-20389
2024-12-01
2530952 byte
Article (Journal)
Electronic Resource
English
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving
ArXiv | 2021
|Deep Federated Learning for Autonomous Driving
IEEE | 2022
|