Straggler-Aware Federated Learning Based on Adaptive Clustering to Support Edge Intelligence

Abstract

Federated learning (FL) has been vigorously promoted in wireless edge networks as it fosters collaborative training of machine learning (ML) models while preserving individual user privacy and data security. In conventional FL, user equipments (UEs) and an aggregator can collaboratively train a globally shared ML model by transmitting ML models instead of raw data. In wireless edge networks, the heterogeneity of multidimensional resources (e.g., computing and communication re-sources) used to transmit ML models may introduce stragglers in FL, characterized by a slow update and/or transmission of local models. The stragglers in FL can significantly degrade learning efficiency and accuracy, as the slowest UE participating in the FL can dramatically slow down entire convergence. In this paper, to alleviate the negative impact of stragglers, we propose a dynamic straggler-aware clustering based FL mechanism, called FeDSC, via adaptive UE clustering. Specifically, we first group participating UEs into multiple clusters based on their computing capability and available wireless resources. Then, we propose an adaptive UE selection scheme to synchronously update the cluster aggregation model. Meanwhile, an edge server performs global aggregation of different cluster models in an asynchronous time-triggered manner. Numerical results show that our proposed FeDSC mechanism can achieve significant performance improvement in terms of training time and model accuracy in comparison to classical FL benchmarks.