MedFD: Personalized and Communication-Efficient Federated Distillation for Heterogeneous Medical Image Analysis

Medical Image classification (MIC) in real-world clinical environments demands models that can operate across decentralized, privacy-sensitive institutions while remaining robust to statistical heterogeneity. Federated Learning (FL) offers a promising solution, yet conventional FL methods often suff...

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Xehetasun bibliografikoak

Egile Nagusiak:	Yuan Sun, Shengfang Sun
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	IEEE 2025-01-01
Saila:	IEEE Access
Gaiak:	Federated learning knowledge distillation medical image analysis parameter-efficient decomposition personalized learning
Sarrera elektronikoa:	https://ieeexplore.ieee.org/document/11155084/
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