Radio and Compute Resource Allocation for SWIPT and RIS-Assisted AirComp Federated Learning

Abstract

Over-the-Air Computation (AirComp) Federated Learning (FL) is actively studied as a communication-efficient technique for distributed Artificial Intelligence (AI) model training. To mitigate the impact of wireless channels on the aggregated global model while addressing client energy sustainability, recent efforts have explored integrating Reconfigurable Intelligent Surfaces (RIS) and Simultaneous Wireless Information and Power Transfer (SWIPT) into AirComp FL. In this context, literature has mainly focused on radio resource allocation for optimized SWIPT and RIS-assisted Downlink (DL) model broadcasting and Uplink (UL) AirComp model aggregation. Nevertheless, existing works largely treat the communication design of AirComp FL in isolation, neglecting the tight coupling between radio and compute resource allocation. In this paper, we address this gap by modeling the radio-compute dependency in AirComp FL and optimizing harvested energy to sustain client-side local training and model transmissions. To this end, we jointly optimize the RIS configuration, SWIPT power-splitting ratio, DL transmission time, and local computing frequency to minimize the total communication and computation overhead in latency and energy. The original non-convex problem is decomposed into two independent subproblems, which are solved iteratively via a combination of low-rank optimization and min-max convex reformulation techniques. Numerical evaluations confirm that integrating RIS and SWIPT into AirComp FL leads to higher accuracy, and reduced latency and energy overheads across the FL pipeline.