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In the medical field, federated learning commonly deals with highlyimbalanced datasets, including skin lesions and gastrointestinal images.Existing federated methods under highly imbalanced datasets primarily focus onoptimizing a global model without incorporating the intra-class variations thatcan arise in medical imaging due to different populations, findings, andscanners. In this paper, we study the inter-client intra-class variations withpublicly available self-supervised auxiliary networks. Specifically, we findthat employing a shared auxiliary pre-trained model, like MoCo-V2, locally onevery client yields consistent divergence measurements. Based on thesefindings, we derive a dynamic balanced model aggregation via self-supervisedpriors (MAS) to guide the global model optimization. Fed-MAS can be utilizedwith different local learning methods for effective model aggregation toward ahighly robust and unbiased global model. Our code is available at\url{https://github.com/xmed-lab/Fed-MAS}.

Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification