Framework for co-distillation driven federated learning to address class imbalance in healthcare

Abstract Federated Learning (FL) is a pioneering approach in distributed machine learning, enabling collaborative model training across multiple clients while retaining data privacy. However, the inherent heterogeneity due to imbalanced resource representations across multiple clients poses signific...

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Principais autores:	Suraj Racha, Shubh Gupta, Humaira Firdowse, Aastik Solanki, Ganesh Ramakrishnan, Kshitij S. Jadhav
Formato:	Artigo
Idioma:	Inglês
Publicado:	Springer 2025-10-01
Series:	Discover Data
Assuntos:	Federated Learning Data Heterogeneity Co-distillation Class Imbalance
Acceso en liña:	https://doi.org/10.1007/s44248-025-00079-7
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