Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networks

arXiv:2404.18287v1 Announce Type: new
Abstract: Federated learning (FL) is a distributed learning paradigm wherein users exchange FL models with a server instead of raw datasets, thereby preserving data privacy and reducing communication overhead. However, the increased number of FL users may hinder completing large-scale FL over wireless networks due to high imposed latency. Cell-free massive multiple-input multiple-output~(CFmMIMO) is a promising architecture for implementing FL because it serves many users on the same time/frequency resources. While CFmMIMO enhances energy efficiency through …

abstract arxiv communication cs.lg data data privacy datasets distributed distributed learning energy federated learning free hinder however latency massive networks optimization paradigm privacy raw scale server type wireless