  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 preserving
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. We tackle the a challenging and yet realistic scenario where each client
is a mobile device containing face images pertaining to only the owner of the
device (one identity per client). Conventional FL algorithms such as FedAvg are
not suitable for this setting because they lead to a trivial solution where all
the face features collapse into a single point in the embedding space. Our
experiments show that FedFace can utilize face images available on 1,000 mobile
devices to enhance the performance of a pre-trained face recognition model,
CosFace, from a TAR of 81.43% to 83.79% on IJB-A (@ 0.1% FAR). For LFW, the
recognition accuracy under the LFW protocol is increased from 99.15% to 99.28%.
FedFace is able to do this while ensuring that the face images are never shared
between devices or between the device and the server. Our code and pre-trained
models will be publicly available.
