Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data

TL;DR

This paper introduces FedGKD, a novel federated learning method that leverages historical global models through adaptive knowledge distillation to mitigate client-drift caused by data and system heterogeneity, leading to faster convergence and higher accuracy.

Contribution

FedGKD is a new approach that fuses knowledge from past global models to improve local training in heterogeneous federated learning environments.

Findings

FedGKD achieves higher accuracy than state-of-the-art methods.

It converges faster with fewer communication rounds.

Effective across various CV/NLP datasets and heterogeneous settings.

Abstract

Federated learning enables multiple clients to collaboratively learn a global model by periodically aggregating the clients' models without transferring the local data. However, due to the heterogeneity of the system and data, many approaches suffer from the "client-drift" issue that could significantly slow down the convergence of the global model training. As clients perform local updates on heterogeneous data through heterogeneous systems, their local models drift apart. To tackle this issue, one intuitive idea is to guide the local model training by the global teachers, i.e., past global models, where each client learns the global knowledge from past global models via adaptive knowledge distillation techniques. Coming from these insights, we propose a novel approach for heterogeneous federated learning, namely FedGKD, which fuses the knowledge from historical global models for local training to alleviate the "client-drift" issue. In this paper, we evaluate FedGKD with extensive experiments on various CV/NLP datasets (i.e., CIFAR-10/100, Tiny-ImageNet, AG News, SST5) and different heterogeneous settings. The proposed method is guaranteed to converge under common assumptions, and achieves superior empirical accuracy in fewer communication runs than five state-of-the-art methods.