# Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification Source: [Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:35:15.802Z Authors: Marawan Elbatel, Hualiang Wang, Robert Mart\\'i, Huazhu Fu, Xiaomeng Li Published: 2023-07-27T15:52:18.000Z ## Abstract 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}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()