Originally published at: https://developer.nvidia.com/blog/federated-learning-with-homomorphic-encryption/
In NVIDIA Clara Train 4.0, we added homomorphic encryption (HE) tools for federated learning (FL). HE enables you to compute data while the data is still encrypted. In Clara Train 3.1, all clients used certified SSL channels to communicate their local model updates with the server. The SSL certificates are needed to establish trusted communication…
In a scenario where the goal is to foster collaboration among competing companies in a market, companies participating as clients in Federated Learning (FL) each hold their own decryption keys to access the updates in the model they receive from the server. However, I’m curious about how the updates encrypted by other clients are handled, given that no client possesses the keys to decrypt another client’s updates. Could someone please clarify this? Thank you!