Federated Learning Using Three-Operator ADMM

arXiv:2211.04152v3 Announce Type: replace
Abstract: Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices. A prominent approach to overcome such difficulties is FedADMM, which is based on the classical two-operator consensus alternating direction method of multipliers (ADMM). …

abstract arxiv communication computational cs.lg data deal devices distributed edge edge devices eess.sp effects federated learning generated instance machine machine learning math.oc paradigm resources type