Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study

arXiv:2403.17287v1 Announce Type: new
Abstract: Federated Learning (FL) emerged as a practical approach to training a model from decentralized data. The proliferation of FL led to the development of numerous FL algorithms and mechanisms. Many prior efforts have given their primary focus on accuracy of those approaches, but there exists little understanding of other aspects such as computational overheads, performance and training stability, etc. To bridge this gap, we conduct extensive performance evaluation on several canonical FL algorithms (FedAvg, FedProx, …

abstract accuracy algorithms arxiv cs.dc cs.lg data decentralized decentralized data development evaluation federated learning focus performance practical prior study training type