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IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
[article]
2023
arXiv
pre-print
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client 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 images from various scanners and patient demographics. This problem becomes even more significant when deploying the global model to unseen clients outside the FL with unseen distributions not
arXiv:2204.08467v2
fatcat:owjboq62ffbvrathjdw445atmu