Private Non-Convex Federated Learning Without a Trusted Server

Understand

We study federated learning (FL) -- especially cross-silo FL -- with non-convex loss functions and data from people who do not trust the server or other silos.

• In this setting, each silo (e.g.
• hospital) must protect the privacy of each person's data (e.g.
• patient's medical record), even if the server or other silos act as adversarial eavesdroppers.