  DNN-based face recognition models require large centrally aggregated face
datasets for training. However, due to the growing data privacy concerns and
legal restrictions, accessing and sharing face datasets has become exceedingly
difficult. We propose FedFace, a federated learning (FL) framework for
collaborative learning of face recognition models in a privacy-aware manner.
FedFace utilizes the face images available on multiple clients to learn an
accurate and generalizable face recognition model where the face images stored
at each client are neither shared with other clients nor the central host and
each client is a mobile device containing face images pertaining to only the
owner of the device (one identity per client). Our experiments show the
effectiveness of FedFace in enhancing the verification performance of
pre-trained face recognition system on standard face verification benchmarks
namely LFW, IJB-A, and IJB-C.
