Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis. (arXiv:2208.11278v1 [cs.LG])

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. …

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