  Unsupervised representation learning has achieved outstanding performances
using centralized data available on the Internet. However, the increasing
awareness of privacy protection limits sharing of decentralized unlabeled image
data that grows explosively in multiple parties (e.g., mobile phones and
cameras). As such, a natural problem is how to leverage these data to learn
visual representations for downstream tasks while preserving data privacy. To
address this problem, we propose a novel federated unsupervised learning
framework, FedU. In this framework, each party trains models from unlabeled
data independently using contrastive learning with an online network and a
target network. Then, a central server aggregates trained models and updates
clients' models with the aggregated model. It preserves data privacy as each
party only has access to its raw data. Decentralized data among multiple
parties are normally non-independent and identically distributed (non-IID),
leading to performance degradation. To tackle this challenge, we propose two
simple but effective methods: 1) We design the communication protocol to upload
only the encoders of online networks for server aggregation and update them
with the aggregated encoder; 2) We introduce a new module to dynamically decide
how to update predictors based on the divergence caused by non-IID. The
predictor is the other component of the online network. Extensive experiments
and ablations demonstrate the effectiveness and significance of FedU. It
outperforms training with only one party by over 5% and other methods by over
14% in linear and semi-supervised evaluation on non-IID data.
