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

Understand

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.