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

• URL: http://arxiv.org/abs/2307.14959v1
• Date: Thu, 27 Jul 2023 15:52:18 GMT
• Title: Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
• Authors: Marawan Elbatel, Hualiang Wang, Robert Mart\'i, Huazhu Fu, Xiaomeng Li
• Abstract summary: In this paper, we study the inter-client intra-class variations with publicly available self-supervised auxiliary networks. We derive a dynamic balanced model aggregation via self-supervised priors to guide the global model optimization.
• Score: 31.633870207003092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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}.

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