CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface
This addresses the challenge of CSIT unavailability in federated edge learning, which is incremental as it adapts existing RIS technology to a specific bottleneck.
The paper tackles the problem of over-the-air model aggregation in federated edge learning systems without channel state information at the transmitters by using reconfigurable intelligent surfaces to align channel coefficients, achieving similar learning accuracy as state-of-the-art CSIT-based methods in image classification experiments.
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.