  To leverage enormous unlabeled data on distributed edge devices, we formulate
a new problem in federated learning called Federated Unsupervised
Representation Learning (FURL) to learn a common representation model without
supervision while preserving data privacy. FURL poses two new challenges: (1)
data distribution shift (Non-IID distribution) among clients would make local
models focus on different categories, leading to the inconsistency of
representation spaces. (2) without the unified information among clients in
FURL, the representations across clients would be misaligned. To address these
challenges, we propose Federated Constrastive Averaging with dictionary and
alignment (FedCA) algorithm. FedCA is composed of two key modules: (1)
dictionary module to aggregate the representations of samples from each client
and share with all clients for consistency of representation space and (2)
alignment module to align the representation of each client on a base model
trained on a public data. We adopt the contrastive loss for local model
training. Through extensive experiments with three evaluation protocols in IID
and Non-IID settings, we demonstrate that FedCA outperforms all baselines with
significant margins.
