Differentially Private Federated Learning via Reconfigurable Intelligent Surface

Abstract

Federated learning (FL), as a disruptive machine learning (ML) paradigm, enables the collaborative training of a global model over decentralized local data sets without sharing them. It spans a wide scope of applications from the Internet of Things (IoT) to biomedical engineering and drug discovery. To support low-latency and high-privacy FL over wireless networks, in this article, we propose a reconfigurable intelligent surface (RIS)-empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy. This is achieved by simultaneously exploiting the channel propagation reconfigurability with RIS for boosting the received signal power, as well as the waveform superposition property with over-the-air computation (AirComp) for fast model aggregation. By considering a practical scenario, where high-dimensional local model updates are transmitted across multiple communication blocks, we characterize the convergence behaviors of the differentially private federated optimization algorithm. We further formulate a system optimization problem to optimize the learning accuracy while satisfying privacy and power constraints via the joint design of transmit power, artificial noise, and phase shifts at RIS, for which a two-step alternating minimization framework is developed. Simulation results validate our systematic, theoretical, and algorithmic achievements and demonstrate that RIS can achieve a better tradeoff between privacy and accuracy for over-the-air FL systems.