Towards Fair and Privacy Preserving Federated Learning for the Healthcare Domain. (arXiv:2308.01529v1 [cs.CR])

Federated learning enables data sharing in healthcare contexts where it might
otherwise be difficult due to data-use-ordinances or security and communication
constraints. Distributed and shared data models allow models to become
generalizable and learn from heterogeneous clients. While addressing data
security, privacy, and vulnerability considerations, data itself is not shared
across nodes in a given learning network. On the other hand, FL models often
struggle with variable client data distributions and operate on an assumption
of independent and identically distributed …

clients communication constraints data data models data security data sharing distributed domain fair federated learning healthcare learn privacy privacy preserving security sharing vulnerability