Unlocking the Potential of Asynchronous Federated Learning in Non-IID Edge Environments

Abstract

Asynchronous Federated Learning (AFL) enhances the efficiency of edge collaborative learning systems by asynchronously aggregating client updates to prevent slowdowns from slow clients. However, in non-IID scenarios, such operations magnify uneven learning among various local data, degrading global model generalization performance on various local data. Prior solutions work by increasing the contribution of certain slow clients in global aggregation but fail to balance efficiency and generalization performance. In this paper, to deal with such a contradiction, we propose an adaptive fine-tuning-based efficient AFL framework, called AFLTuning, which enables certain clients to contribute high-quality local updates more frequently, thereby facilitating rapid and balanced global learning without introducing additional delay. Specifically, we first propose a data distribution-driven underrepresented client identification mechanism to recognize clients that may have highly skewed data distributions or infrequent participation in global aggregation due to long local processing times, causing the global model to poorly learn their local data. Then, we develop a prediction calibration-based adaptive fine-tuning mechanism to improve these clients’ training efficiency and quality, and a frequency-aware weighted aggregation mechanism to strategically increase their weights during global aggregation. These designs allow underrepresented clients to participate more frequently and contribute more to the global model learning without introducing additional delay, thus mitigating the uneven learning and improving the generalization of AFL while maintaining system efficiency. Extensive experiments demonstrate AFLTuning’s superiority to state-of-the-art methods in both performance and efficiency.