A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency

arXiv:2307.10655v2 Announce Type: replace-cross
Abstract: Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a privacy-preserving manner. This approach has gained considerable attention, promoting numerous surveys to summarize the related works. However, the majority of these surveys concentrate on FL methods that share model parameters during the training process, while overlooking the possibility of sharing local information in other forms. In this paper, we …

arxiv attention centralization clients communication cs.cr cs.lg data efficiency federated federated learning information local paradigm perspectives privacy share survey surveys training utility