HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning

arXiv:2403.04207v1 Announce Type: new
Abstract: Federated Learning (FL) is a practical approach to train deep learning models collaboratively across user-end devices, protecting user privacy by retaining raw data on-device. In FL, participating user-end devices are highly fragmented in terms of hardware and software configurations. Such fragmentation introduces a new type of data heterogeneity in FL, namely \textit{system-induced data heterogeneity}, as each device generates distinct data depending on its hardware and software configurations. In this paper, we first characterize the impact …

abstract arxiv cs.dc cs.lg data deep learning devices federated learning fragmentation hardware practical privacy raw software terms train type