Integrated user scheduling and beam steering in over-the-air federated learning for mobile IoT

TL;DR

This paper introduces an integrated user scheduling and beam steering method for over-the-air federated learning in mobile IoT, improving communication efficiency and model accuracy in resource-constrained wireless networks.

Contribution

It proposes a novel joint optimization framework for user scheduling and beam steering, including a low-complexity policy based on wireless channel characteristics.

Findings

Enhanced aggregation accuracy compared to existing methods

Improved learning performance in large-scale IoT networks

Reduced computational complexity with the proposed scheduling policy

Abstract

The rising popularity of Internet of things (IoT) has spurred technological advancements in mobile internet and interconnected systems. While offering flexible connectivity and intelligent applications across various domains, IoT service providers must gather vast amounts of sensitive data from users, which nonetheless concomitantly raises concerns about privacy breaches. Federated learning (FL) has emerged as a promising decentralized training paradigm to tackle this challenge. This work focuses on enhancing the aggregation efficiency of distributed local models by introducing over-the-air computation into the FL framework. Due to radio resource scarcity in large-scale networks, only a subset of users can participate in each training round. This highlights the need for effective user scheduling and model transmission strategies to optimize communication efficiency and inference accuracy. To address this, we propose an integrated approach to user scheduling and receive beam steering, subject to constraints on the number of selected users and transmit power. Leveraging the difference-of-convex technique, we decompose the primal non-convex optimization problem into two sub-problems, yielding an iterative solution. While effective, the computational load of the iterative method hampers its practical implementation. To overcome this, we further propose a low-complexity user scheduling policy based on characteristic analysis of the wireless channel to directly determine the user subset without iteration. Extensive experiments validate the superiority of the proposed method in terms of aggregation error and learning performance over existing approaches.