Federated Model Aggregation via Self-Supervised Priors for Highly
  Imbalanced Medical Image Classification
Marawan Elbatel Hualiang Wang Robert Martí Huazhu Fu Xiaomeng Li
In the medical field, federated learning commonly deals with highly
imbalanced datasets, including skin lesions and gastrointestinal images.
Existing federated methods under highly imbalanced datasets primarily focus on
optimizing a global model without incorporating the intra-class variations that
can arise in medical imaging due to different populations, findings, and
scanners. In this paper, we study the inter-client intra-class variations with
publicly available self-supervised auxiliary networks. Specifically, we find
that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on
every client yields consistent divergence measurements. Based on these
findings, we derive a dynamic balanced model aggregation via self-supervised
priors (MAS) to guide the global model optimization. Fed-MAS can be utilized
with different local learning methods for effective model aggregation toward a
highly robust and unbiased global model. Our code is available at
\url{https://github.com/xmed-lab/Fed-MAS}.