Covert Federated Learning via Intelligent Reflecting Surfaces

Abstract

Over-the-air computation (OAC) is a promising technology that can achieve rapid model aggregation by utilizing the wireless waveform superposition feature to harness the interference of multiple-access channel for wireless federated learning (FL). However, OAC-based aggregation for OAC faces critical security challenges due to unfavorable and wireless broadcast properties, such as privacy leaks and eavesdropping attacks. In this paper, we propose to utilize an intelligent reflecting surface (IRS) to support covert OAC-based FL. We first derive the optimal condition for covertness in OAC with IRS and formulate a joint optimization problem to select the maximum covert devices participating in the model aggregation while satisfying the mean squared error (MSE) requirement. We then design a covert difference-of-convex-functions program (CDC) to efficiently determine the transmission power of the device, aggregation beamforming of base station (BS), phase shifts, and reflection amplitudes at the IRS. Simulation results demonstrate that our proposed approach can achieve significant performance gain compared to the baseline algorithms by deploying IRS into covert OAC-based FL.