  Federated Learning (FL) for face recognition aggregates locally optimized
models from individual clients to construct a generalized face recognition
model. However, previous studies present two major challenges: insufficient
incorporation of self-supervised learning and the necessity for clients to
accommodate multiple subjects. To tackle these limitations, we propose FedFS
(Federated Learning for personalized Face recognition via intra-subject
Self-supervised learning framework), a novel federated learning architecture
tailored to train personalized face recognition models without imposing
subjects. Our proposed FedFS comprises two crucial components that leverage
aggregated features of the local and global models to cooperate with
representations of an off-the-shelf model. These components are (1) adaptive
soft label construction, utilizing dot product operations to reformat labels
within intra-instances, and (2) intra-subject self-supervised learning,
employing cosine similarity operations to strengthen robust intra-subject
representations. Additionally, we introduce a regularization loss to prevent
overfitting and ensure the stability of the optimized model. To assess the
effectiveness of FedFS, we conduct comprehensive experiments on the DigiFace-1M
and VGGFace datasets, demonstrating superior performance compared to previous
methods.
