Federated learning facilitates the collaborative learning of a global model across multiple
distributed medical institutions without centralizing data. Nevertheless the expensive cost of …

In the medical field, federated learning commonly deals with highly imbalanced datasets,
including skin lesions and gastrointestinal images. Existing federated methods under highly …

The federated learning privacy-preserving framework has achieved fruitful results in training
deep models across clients. This survey aims to provide a systematic overview of federated …

Distributed learning is a promising alternative to central learning for machine learning (ML)
model training, overcoming data-sharing problems in healthcare. Previous studies exploring …

In the medical field, federated learning commonly deals with highly imbalanced datasets,
including skin lesions and gastrointestinal images. Existing federated methods under highly …

Supplementary Material of “Think Twice Before Selection: Federated Evidential Active Learning
for Medical Image Analysis with Page 1 Supplementary Material of “Think Twice Before …