Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning. (arXiv:2202.07470v1 [cs.LG])

Deep learning models have been deployed in an increasing number of edge and
mobile devices to provide healthcare. These models rely on training with a
tremendous amount of labeled data to achieve high accuracy. However, for
medical applications such as dermatological disease diagnosis, the private data
collected by mobile dermatology assistants exist on distributed mobile devices
of patients, and each device only has a limited amount of data. Directly
learning from limited data greatly deteriorates the performance of learned
models. …

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