  Face recognition has been extensively studied in computer vision and
artificial intelligence communities in recent years. An important issue of face
recognition is data privacy, which receives more and more public concerns. As a
common privacy-preserving technique, Federated Learning is proposed to train a
model cooperatively without sharing data between parties. However, as far as we
know, it has not been successfully applied in face recognition. This paper
proposes a framework named FedFace to innovate federated learning for face
recognition. Specifically, FedFace relies on two major innovative algorithms,
Partially Federated Momentum (PFM) and Federated Validation (FV). PFM locally
applies an estimated equivalent global momentum to approximating the
centralized momentum-SGD efficiently. FV repeatedly searches for better
federated aggregating weightings via testing the aggregated models on some
private validation datasets, which can improve the model's generalization
ability. The ablation study and extensive experiments validate the
effectiveness of the FedFace method and show that it is comparable to or even
better than the centralized baseline in performance.
