IPFed: A Privacy-Preserving Federated Learning Framework for Face Verification

In this paper, we proposed a new method to solve the privacy and accuracy problems described in Sec.II. We called our proposed method Identity Protected Federated Learning (IPFed) In IPFed, the class embedding is multiplied by a random transformation parameter which is secret to the learning server. This makes it possible to perform the optimization while keeping the class embeddeddings secret from any server.