  Unsupervised domain adaptation has been widely adopted to generalize models
for unlabeled data in a target domain, given labeled data in a source domain,
whose data distributions differ from the target domain. However, existing works
are inapplicable to face recognition under privacy constraints because they
require sharing sensitive face images between two domains. To address this
problem, we propose a novel unsupervised federated face recognition approach
(FedFR). FedFR improves the performance in the target domain by iteratively
aggregating knowledge from the source domain through federated learning. It
protects data privacy by transferring models instead of raw data between
domains. Besides, we propose a new domain constraint loss (DCL) to regularize
source domain training. DCL suppresses the data volume dominance of the source
domain. We also enhance a hierarchical clustering algorithm to predict pseudo
labels for the unlabeled target domain accurately. To this end, FedFR forms an
end-to-end training pipeline: (1) pre-train in the source domain; (2) predict
pseudo labels by clustering in the target domain; (3) conduct
domain-constrained federated learning across two domains. Extensive experiments
and analysis on two newly constructed benchmarks demonstrate the effectiveness
of FedFR. It outperforms the baseline and classic methods in the target domain
by over 4% on the more realistic benchmark. We believe that FedFR will shed
light on applying federated learning to more computer vision tasks under
privacy constraints.
