REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments

arXiv:2308.13662v2 Announce Type: replace
Abstract: Federated Learning (FL) plays a critical role in distributed systems. In these systems, data privacy and confidentiality hold paramount importance, particularly within edge-based data processing systems such as IoT devices deployed in smart homes. FL emerges as a privacy-enforcing sub-domain of machine learning that enables model training on client devices, eliminating the necessity to share private data with a central server. While existing research has predominantly addressed challenges pertaining to data heterogeneity, there remains a …

abstract arxiv cs.dc cs.lg data data privacy data processing devices distributed distributed systems domain edge environments federated learning framework homes importance iot machine machine learning privacy processing role smart smart homes systems training type