Abstract
Federated learning enables diverse distributed medical institutions to learn a global prediction model collaboratively while preserving data privacy. However, in practical clinical deployment, the global model may suffer from degraded generalization performance due to the high heterogeneity of medical data from different sites. In this paper, we propose and implement a robust framework for medical image segmentation (FML-MIS) via a federated meta-learning scheme, which can achieve effective privacy preservation and model generalization. Our scheme consists of three components: (1) a federated domain image generation mechanism that synthesizes diverse and realistic images across domains; (2) a meta-learning strategy that leverages the generated images to enhance the model’s adaptability to new domains; and (3) a differential privacy mechanism that protects the model’s security and resists inference attacks during federated training. We evaluate our scheme in a federated learning setting on three public datasets. Extensive experiments and comparison results show that our scheme can effectively utilize rich multi-source data and improve the generalization ability of the model while preserving data privacy. The project code is available at https://github.com/yuwxl/FML-MIS.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China under Grant No. 61971004, the Natural Science Foundation of Anhui Province, China (Grant No. 2008085MF190), the Equipment Advanced Research Project (Sharing Technology), China (Grant No. 80912020104), and the University Synergy Innovation Program of Anhui Province, China (NO. GXXT-2022-044).
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Yu, M., Liu, H. (2023). FML-MIS: A Scheme of Privacy Protection and Model Generalization for Medical Images Segmentation via Federated Meta-learning. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14358. Springer, Cham. https://doi.org/10.1007/978-3-031-46314-3_11
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