When the Curious Abandon Honesty: Federated Learning Is Not Private

Understand

In federated learning (FL), data does not leave personal devices when they are jointly training a machine learning model.

• Instead, these devices share gradients, parameters, or other model updates, with a central party (e.g., a company) coordinating the training.
• Because data never "leaves" personal devices, FL is often presented as privacy-preserving.
• Yet, recently it was shown that this protection is but a thin facade, as even a passive, honest-but-curious attacker observing gradients can reconstruct data of individual users contributing to the protocol.