CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface

TL;DR

This paper introduces a CSIT-free model aggregation method for federated edge learning using reconfigurable intelligent surfaces to align channels, enabling effective learning without channel state information at transmitters.

Contribution

It proposes a novel RIS-based approach and a difference-of-convex algorithm to optimize channel alignment in CSIT-free federated learning systems.

Findings

Achieves similar accuracy to CSIT-based methods in image classification.

Demonstrates effective channel alignment without CSIT.

Provides an efficient optimization algorithm for non-convex problems.

Abstract

We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable. We leverage the reconfigurable intelligent surface (RIS) technology to align the cascaded channel coefficients for CSIT-free model aggregation. To this end, we jointly optimize the RIS and the receiver by minimizing the aggregation error under the channel alignment constraint. We then develop a difference-of-convex algorithm for the resulting non-convex optimization. Numerical experiments on image classification show that the proposed method is able to achieve a similar learning accuracy as the state-of-the-art CSIT-based solution, demonstrating the efficiency of our approach in combating the lack of CSIT.