FLAV: Federated Learning for Autonomous Vehicle privacy protection

Abstract

Autonomous Vehicle Systems are committed to safer, more efficient and more convenient transportation on the roads of the future. However, concerns about vehicle data privacy and security remain significant. Federated Learning, as a decentralized machine learning approach, allows multiple devices or data sources to collaboratively train models without sharing raw data, providing essential privacy protection. In this paper, we propose a privacy-preserving framework for autonomous vehicles, named FLAV. First, we use a multi-chain parallel aggregation strategy to transmit model parameters and design a model parameter filtering mechanism, which reduces communication overhead by filtering out the local model parameters of certain vehicles, thereby alleviating bandwidth pressure. Second, we introduce a dynamic adjustment mechanism that automatically adjusts the regularization strength in the loss function by comparing the differences between each vehicle’s local model parameters and the cumulative parameters from the preceding vehicles in the chain. This mechanism effectively balances local model training with global model consistency, ensuring the model’s adaptability to local data while enhancing the coordination of models between vehicles in the chain. Experimental results demonstrate that our proposed method reduces communication costs while improving model accuracy and privacy protection, effectively ensuring the security of autonomous driving data.