Power-Efficient Over-the-Air Aggregation with Receive Beamforming for Federated Learning

2025-01-29

AI Summary

To address excessive uplink power consumption in over-the-air computation-based federated learning (AirComp-FL), this paper jointly optimizes device transmit scaling factors and multi-antenna receive beamforming at the server, minimizing total transmit power while guaranteeing model convergence. We first establish an explicit theoretical connection between aggregation error and FL convergence behavior. Based on this, we formulate a joint optimization problem with a bi-convex structure and design a channel state information (CSI) error-aware alternating optimization algorithm. The proposed method achieves both robustness against CSI imperfections and computational tractability. Experiments on standard classification datasets demonstrate that, compared to baseline schemes, it reduces total device transmit power by 40%–65%, while strictly preserving model accuracy and convergence rate.

Technology Category

• Machine Learning: Optimization
• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements

Abstract

This paper studies power-efficient uplink transmission design for federated learning (FL) that employs over-the-air analog aggregation and multi-antenna beamforming at the server. We jointly optimize device transmit weights and receive beamforming at each FL communication round to minimize the total device transmit power while ensuring convergence in FL training. Through our convergence analysis, we establish sufficient conditions on the aggregation error to guarantee FL training convergence. Utilizing these conditions, we reformulate the power minimization problem into a unique bi-convex structure that contains a transmit beamforming optimization subproblem and a receive beamforming feasibility subproblem. Despite this unconventional structure, we propose a novel alternating optimization approach that guarantees monotonic decrease of the objective value, to allow convergence to a partial optimum. We further consider imperfect channel state information (CSI), which requires accounting for the channel estimation errors in the power minimization problem and FL convergence analysis. We propose a CSI-error-aware joint beamforming algorithm, which can substantially outperform one that does not account for channel estimation errors. Simulation with canonical classification datasets demonstrates that our proposed methods achieve significant power reduction compared to existing benchmarks across a wide range of parameter settings, while attaining the same target accuracy under the same convergence rate.

Problem

• Energy-efficient transmission
• Federated Learning
• Data Integrity

Innovation

• Energy-efficient transmission
• Federated Learning
• Robust algorithm design