FedFace: Collaborative Learning of Face Recognition Model
This addresses privacy issues in face recognition for mobile device users, but it is incremental as it applies existing federated learning to a specific domain.
The authors tackled the problem of training face recognition models without centralized data sharing due to privacy concerns, proposing FedFace, a federated learning framework that improved verification performance on benchmarks like LFW, IJB-A, and IJB-C.
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.