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.
Communication-efficient learning of deep networks from decentralized data
2 Pith papers cite this work. Polarity classification is still indexing.
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FedPF frames privacy and fairness in federated learning as a zero-sum game, shows privacy reduces bias-detection power under finite samples, and cuts discrimination up to 42.9% while retaining competitive accuracy.
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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.
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FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility
FedPF frames privacy and fairness in federated learning as a zero-sum game, shows privacy reduces bias-detection power under finite samples, and cuts discrimination up to 42.9% while retaining competitive accuracy.