  Discriminative unsupervised learning methods such as contrastive learning
have demonstrated the ability to learn generalized visual representations on
centralized data. It is nonetheless challenging to adapt such methods to a
distributed system with unlabeled, private, and heterogeneous client data due
to user styles and preferences. Federated learning enables multiple clients to
collectively learn a global model without provoking any privacy breach between
local clients. On the other hand, another direction of federated learning
studies personalized methods to address the local heterogeneity. However, work
on solving both generalization and personalization without labels in a
decentralized setting remains unfamiliar. In this work, we propose a novel
method, FedStyle, to learn a more generalized global model by infusing local
style information with local content information for contrastive learning, and
to learn more personalized local models by inducing local style information for
downstream tasks. The style information is extracted by contrasting original
local data with strongly augmented local data (Sobel filtered images). Through
extensive experiments with linear evaluations in both IID and non-IID settings,
we demonstrate that FedStyle outperforms both the generalization baseline
methods and personalization baseline methods in a stylized decentralized
setting. Through comprehensive ablations, we demonstrate our design of style
infusion and stylized personalization improve performance significantly.
