QoS-Constrained Federated Learning Empowered by Intelligent Reflecting Surface (2103.11551v1)

Abstract: This paper investigates the model aggregation process in an over-the-air federated learning (AirFL) system, where an intelligent reflecting surface (IRS) is deployed to assist the transmission from users to the base station (BS). With the purpose of overcoming the absence of the security examination against malicious individuals, successive interference cancellation (SIC) is adopted as a basis to support analyzing statistic characteristics of model parameters from devices. The objective of this paper is to minimize the mean-square-error by jointly optimizing the receive beamforming vector at the BS, transmit power allocation at users, and phase shift matrix of the IRS, subject to the transmit power constraint for devices, unit-modulus constraint for reflecting elements, SIC decoding order constraint and quality-of-service constraint. To address this complicated problem, alternating optimization is employed to decompose it into three subproblems, where the optimal receive beamforming vector is obtained by solving the first subproblem with the Lagrange dual method. Then, the convex relaxation method is applied to the transmit power allocation subproblem to find a suboptimal solution. Eventually, the phase shift matrix subproblem is addressed by invoking the semidefinite relaxation. Simulation results validate the availability of IRS and the effectiveness of the proposed scheme in improving federated learning performance.