Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

arXiv:2102.12920v4 Announce Type: replace
Abstract: Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, …

abstract aggregation arxiv collection computation cs.dc cs.lg data data collection federated learning frameworks fusion paradigm survey training trends type via