Quantization and Privacy Noise Co-Design for Utility-Privacy-Communication Trade-off in Federated Learning

This study addresses the core challenges in federated learning (FL), namely achieving optimal model utility, safeguarding local data privacy, and maintaining efficient communication. While previous research has focused on either the privacy-utility or communication-utility trade-offs, the investigation of simultaneously considering utility, privacy protection, and communication efficiency has been largely overlooked. In this paper, we propose a novel training framework for FL that combines communication efficiency and differential privacy. Specifically, we employ quantization and binomial noise on model updates to enhance privacy protection and communication efficiency concurrently. Through convergence and privacy analysis, we formulate an optimization problem that maximizes model utility while adhering to privacy and communication constraints. Additionally, we introduce an adaptive algorithm to determine key system parameters, including the level of quantization and privacy noise. Simulation results validate the effectiveness of our proposed FL framework and parameter optimization algorithm.