A robust and personalized privacy-preserving approach for adaptive clustered federated distillation

A robust and personalized privacy-preserving approach for adaptive clustered federated distillation

Abstract Federated learning (FL) is a promising approach that addresses privacy, and scalability concerns in contrast to traditional centralized methods. Challenges such as personalization and data heterogeneity issues remain critical. Clustered federated learning (CFL) has been proposed as a promis...

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Bibliographic Details
Main Authors:	Mai Shawkat, Zainab H. Ali, Mofreh Salem, Ali El-desoky
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-04-01
Series:	Scientific Reports
Subjects:	Deep learning Federated learning Knowledge distillation Meta learning Privacy protection
Online Access:	https://doi.org/10.1038/s41598-025-96468-8
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