Heterogeneous Federated Learning via Generative Model-Aided Knowledge Distillation in the Edge

Federated Learning (FL) has been popular recently as a framework for training Machine Learning (ML) models in a distributed and privacy-preserving manner. Traditional FL frameworks often struggle with model and statistical heterogeneity among participating clients, impacting learning performance and...

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Veröffentlicht in:	IEEE internet of things journal 2024-10, p.1-1
Hauptverfasser:	Sun, Chuanneng, Jiang, Tingcong, Pompili, Dario
Format:	Artikel
Sprache:	eng
Schlagworte:	Computational modeling Convergence Data models Distributed databases edge learning Federated learning Internet of Things knowledge distillation model heterogeneity Privacy Radio access networks Servers Training
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