Joint Accuracy and Latency Optimization for Quantized Federated Learning in Vehicular Networks

Nowadays, vehicular networks have emerged as a boosting technology to enhance traffic efficiency and safety within transportation systems. As the amount of onboard data increases and data privacy concerns grow, federated learning (FL) has gained popularity for harnessing the data for intelligent transportation operations. To satisfy the strict latency criteria in vehicular networks, a quantization scheme is employed within FL to reduce the size of local models before uplink transmission. In this paper, considering the nature of vehicles’ high mobility, we aim to optimize both the learning performance and latency simultaneously by jointly considering the communication resource budget and quantization strategies. Specifically, we first analyze the convergence performance of the quantized FL, which demonstrates the effects of both quantization error and the number of clients on the convergence rate. Then, we formulate a multi-objective optimization problem (MOP) to maximize the number of participating clients and minimize the overall latency, by jointly optimizing the quantization level, wireless resource allocation and client selection. To deal with the MOP, we decompose the MOP into a set of scalar optimization subproblems, each formulated as a Markov Decision Process (MDP). To solve the MDP in high-mobile vehicular networks, we propose a novel deep reinforcement learning-based vehicle heterogeneous quantization FL (DRL-VQFL) method, which leverages a DRL framework built upon the proximal policy optimization algorithm. Then, a parameter transfer strategy is employed to solve the neighboring subproblems efficiently. Our extensive simulations demonstrate the effectiveness and efficiency of the DRL-VQFL approach, showcasing its superiority over other benchmark methods.