Client Selection for Federated Learning in Vehicular Edge Computing: A Deep Reinforcement Learning Approach

Abstract

Vehicular edge computing (VEC) has emerged as a solution that places computing resources at the edge of the network to address resource management, service continuity, and scalability issues in dynamic vehicular environments. However, VEC faces challenges such as task offloading, varying communication conditions, and data security. To tackle these challenges, federated learning (FL), a distributed machine learning framework that allows multiple clients to collaboratively train a global model without sharing their data, is utilized. However, vehicular clients have characteristics such as non-independent and identically distributed (non-IID) data, diverse communication capabilities, and high mobility, which pose difficulties for model convergence. A dynamic and optimal client selection method is required to address VEC and FL challenges. Therefore, in this paper, we propose a distributed client selection method with multi-objectives that can dynamically adapt to changing conditions. This method combines fuzzy logic with deep reinforcement learning (DRL) based deep Q-network (DQN). Initially, the fuzzy logic approach infers client candidates based on the stability of communication links. Subsequently, the DQN approach selects the final clients by considering the objectives of maximizing model accuracy and minimizing processing time and communication overhead. Unlike conventional methods, the proposed method provides an efficient solution that balances different objectives and improves model performance by ensuring comprehensive network coverage. Consequently, the proposed method achieves higher model accuracy, lower processing time, and reduced communication overhead compared to conventional methods.