  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. Federated learning (FL) can train models by using data distributed on
devices while keeping the data local for privacy. Existing works on FL assume
all the data have ground-truth labels. However, medical data often comes
without any accompanying labels since labeling requires expertise and results
in prohibitively high labor costs. The recently developed self-supervised
learning approach, contrastive learning (CL), can leverage the unlabeled data
to pre-train a model, after which the model is fine-tuned on limited labeled
data for dermatological disease diagnosis. However, simply combining CL with FL
as federated contrastive learning (FCL) will result in ineffective learning
since CL requires diverse data for learning but each device only has limited
data. In this work, we propose an on-device FCL framework for dermatological
disease diagnosis with limited labels. Features are shared in the FCL
pre-training process to provide diverse and accurate contrastive information.
After that, the pre-trained model is fine-tuned with local labeled data
independently on each device or collaboratively with supervised federated
learning on all devices. Experiments on dermatological disease datasets show
that the proposed framework effectively improves the recall and precision of
dermatological disease diagnosis compared with state-of-the-art methods.
