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IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation. (arXiv:2204.08467v1 [eess.IV])
April 20, 2022, 1:12 a.m. | Meirui Jiang, Hongzheng Yang, Chen Cheng, Qi Dou
cs.LG updates on arXiv.org arxiv.org
Federated learning (FL) allows multiple medical institutions to
collaboratively learn a global model without centralizing all clients data. It
is difficult, if possible at all, for such a global model to commonly achieve
optimal performance for each individual client, due to the heterogeneity of
medical data from various scanners and patient demographics. This problem
becomes even more significant when deploying the global model to unseen clients
outside the FL with new distributions not presented during federated training.
To optimize the …
More from arxiv.org / cs.LG updates on arXiv.org
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