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Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation
Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches assume a homogeneous compute budget across institutions, limiting participation of low-resource sites. We propose Fed-ADApt, a depth-adaptive federate
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