Improving Wireless Federated Learning via Joint Downlink-Uplink Beamforming over Analog Transmission

TL;DR

This paper proposes a joint downlink-uplink beamforming method to improve wireless federated learning convergence, effectively handling noisy channels and optimizing communication over time-varying wireless links.

Contribution

It introduces a low-complexity greedy algorithm for joint beamforming design that enhances FL training convergence over wireless channels.

Findings

JDUBF outperforms conventional beamforming methods.

The proposed algorithm converges quickly due to efficient initialization.

Simulation results validate the effectiveness of the joint beamforming approach.

Abstract

Federated learning (FL) over wireless networks using analog transmission can efficiently utilize the communication resource but is susceptible to errors caused by noisy wireless links. In this paper, assuming a multi-antenna base station, we jointly design downlink-uplink beamforming to maximize FL training convergence over time-varying wireless channels. We derive the round-trip model updating equation and use it to analyze the FL training convergence to capture the effects of downlink and uplink beamforming and the local model training on the global model update. Aiming to maximize the FL training convergence rate, we propose a low-complexity joint downlink-uplink beamforming (JDUBF) algorithm, which adopts a greedy approach to decompose the multi-round joint optimization and convert it into per-round online joint optimization problems. The per-round problem is further decomposed into three subproblems over a block coordinate descent framework, where we show that each subproblem can be efficiently solved by projected gradient descent with fast closed-form updates. An efficient initialization method that leads to a closed-form initial point is also proposed to accelerate the convergence of JDUBF. Simulation demonstrates that JDUBF substantially outperforms the conventional separate-link beamforming design.