  In dermatological disease diagnosis, the private data collected by mobile
dermatology assistants exist on distributed mobile devices of patients.
Federated learning (FL) can use decentralized data to train models while
keeping data local. Existing FL methods assume all the data have labels.
However, medical data often comes without full labels due to high labeling
costs. Self-supervised learning (SSL) methods, contrastive learning (CL) and
masked autoencoders (MAE), can leverage the unlabeled data to pre-train models,
followed by fine-tuning with limited labels. However, combining SSL and FL has
unique challenges. For example, CL requires diverse data but each device only
has limited data. For MAE, while Vision Transformer (ViT) based MAE has higher
accuracy over CNNs in centralized learning, MAE's performance in FL with
unlabeled data has not been investigated. Besides, the ViT synchronization
between the server and clients is different from traditional CNNs. Therefore,
special synchronization methods need to be designed. In this work, we propose
two federated self-supervised learning frameworks for dermatological disease
diagnosis with limited labels. The first one features lower computation costs,
suitable for mobile devices. The second one features high accuracy and fits
high-performance servers. Based on CL, we proposed federated contrastive
learning with feature sharing (FedCLF). Features are shared for diverse
contrastive information without sharing raw data for privacy. Based on MAE, we
proposed FedMAE. Knowledge split separates the global and local knowledge
learned from each client. Only global knowledge is aggregated for higher
generalization performance. Experiments on dermatological disease datasets show
superior accuracy of the proposed frameworks over state-of-the-arts.
