Combating Interference for Over-the-Air Federated Learning: A Statistical Approach via RIS

TL;DR

This paper introduces a RIS-based statistical approach to enhance interference robustness in over-the-air federated learning, achieving unbiased gradient estimation and improved convergence through phase manipulation and power control schemes.

Contribution

It proposes novel RIS phase shift and power control schemes for interference suppression in AirComp-enabled federated learning, with theoretical analysis and validation.

Findings

Error variance reduces as 1/N with more RIS elements.

Proposed schemes outperform existing baselines.

Convergence rate approaches ideal without interference as N increases.

Abstract

Over-the-air computation (AirComp) integrates analog communication with task-oriented computation, serving as a key enabling technique for communication-efficient federated learning (FL) over wireless networks. However, owing to its analog characteristics, AirComp-enabled FL (AirFL) is vulnerable to both unintentional and intentional interference. In this paper, we aim to attain robustness in AirComp aggregation against interference via reconfigurable intelligent surface (RIS) technology to artificially reconstruct wireless environments. Concretely, we establish performance objectives tailored for interference suppression in wireless FL systems, aiming to achieve unbiased gradient estimation and reduce its mean square error (MSE). Oriented at these objectives, we introduce the concept of phase-manipulated favorable propagation and channel hardening for AirFL, which relies on the adjustment of RIS phase shifts to realize statistical interference elimination and reduce the error variance of gradient estimation. Building upon this concept, we propose two robust aggregation schemes of power control and RIS phase shifts design, both ensuring unbiased gradient estimation in the presence of interference. Theoretical analysis of the MSE and FL convergence affirms the anti-interference capability of the proposed schemes. It is observed that computation and interference errors diminish by an order of $\mathcal{O}\left(\frac{1}{N}\right)$ where $N$ is the number of RIS elements, and the ideal convergence rate without interference can be asymptotically achieved by increasing $N$. Numerical results confirm the analytical results and validate the superior performance of the proposed schemes over existing baselines.