Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data. User heterogeneity has imposed significant challenges to FL, which can incur drifted global models that are slow to co...

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Veröffentlicht in:	arXiv.org 2021-06
Hauptverfasser:	Zhu, Zhuangdi, Hong, Junyuan, Zhou, Jiayu
Format:	Artikel
Sprache:	eng
Schlagworte:	Distillation Heterogeneity Knowledge Machine learning Mathematical models Parameters
Online-Zugang:	Volltext
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