SplitFed-CL improves segmentation performance in privacy-preserving federated settings by having a global teacher refine unreliable local labels via weighted student-teacher correction, consistency regularization, and adaptive loss weighting.
Improving multi- ple sclerosis lesion segmentation across clinical sites: A feder- ated learning approach with noise-resilient training
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SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labels
SplitFed-CL improves segmentation performance in privacy-preserving federated settings by having a global teacher refine unreliable local labels via weighted student-teacher correction, consistency regularization, and adaptive loss weighting.