Load Balancing in Federated Learning

TL;DR

This paper introduces a load balancing approach for federated learning that uses a load metric based on Age of Information and a decentralized Markov scheduling policy to improve fairness, efficiency, and convergence.

Contribution

It proposes a novel load metric and a decentralized scheduling policy that reduces load variance and enhances federated learning performance.

Findings

Reduced load metric variance improves fairness and efficiency.

Decentralized Markov scheduling eliminates management overhead.

Enhanced convergence rate of learning models.

Abstract

Federated Learning (FL) is a decentralized machine learning framework that enables learning from data distributed across multiple remote devices, enhancing communication efficiency and data privacy. Due to limited communication resources, a scheduling policy is often applied to select a subset of devices for participation in each FL round. The scheduling process confronts significant challenges due to the need for fair workload distribution, efficient resource utilization, scalability in environments with numerous edge devices, and statistically heterogeneous data across devices. This paper proposes a load metric for scheduling policies based on the Age of Information and addresses the above challenges by minimizing the load metric variance across the clients. Furthermore, a decentralized Markov scheduling policy is presented, that ensures a balanced workload distribution while eliminating the management overhead irrespective of the network size due to independent client decision-making. We establish the optimal parameters of the Markov chain model and validate our approach through simulations. The results demonstrate that reducing the load metric variance not only promotes fairness and improves operational efficiency, but also enhances the convergence rate of the learning models.