  Federated Learning (FL) enables multiple institutes to train models
collaboratively without sharing private data. Current FL research focuses on
communication efficiency, privacy protection, and personalization and assumes
that the data of FL have already been ideally collected. In medical scenarios,
however, data annotation demands both expertise and intensive labor, which is a
critical problem in FL. Active learning (AL), has shown promising performance
in reducing the number of data annotations in medical image analysis. We
propose a federated AL (FedAL) framework in which AL is executed periodically
and interactively under FL. We exploit a local model in each hospital and a
global model acquired from FL to construct an ensemble. We use
ensemble-entropy-based AL as an efficient data-annotation strategy in FL.
Therefore, our FedAL framework can decrease the amount of annotated data and
preserve patient privacy while maintaining the performance of FL. To our
knowledge, this is the first FedAL framework applied to medical images. We
validated our framework on real-world dermoscopic datasets. Using only 50% of
samples, our framework was able to achieve state-of-the-art performance on a
skin-lesion classification task. Our framework performed better than several
state-of-the-art AL methods under FL and achieved comparable performance to
full-data FL.
