Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning

TL;DR

This paper introduces FedRank, a ranking-based client selection method for federated learning that uses imitation learning to improve model accuracy, convergence speed, and energy efficiency across heterogeneous devices.

Contribution

FedRank is a novel, end-to-end ranking approach that leverages imitation learning for adaptive client selection in federated learning, addressing cold-start and heterogeneity challenges.

Findings

Boosts model accuracy by 5.2% to 56.9%.

Accelerates training convergence up to 2.01 times.

Reduces energy consumption by up to 40.1%.

Abstract

Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round critically affects both the model performance and training efficiency, especially given the vast heterogeneity in training capabilities and data distribution across devices. To address these challenges, we introduce a novel device selection solution called FedRank, which is an end-to-end, ranking-based approach that is pre-trained by imitation learning against state-of-the-art analytical approaches. It not only considers data and system heterogeneity at runtime but also adaptively and efficiently chooses the most suitable clients for model training. Specifically, FedRank views client selection in FL as a ranking problem and employs a pairwise training strategy for the smart selection process. Additionally, an imitation learning-based approach is designed to counteract the cold-start issues often seen in state-of-the-art learning-based approaches. Experimental results reveal that \model~ boosts model accuracy by 5.2\% to 56.9\%, accelerates the training convergence up to $2.01 \times$ and saves the energy consumption up to $40.1\%$.