IFL-GAN: Improved Federated Learning Generative Adversarial Network With Maximum Mean Discrepancy Model Aggregation

The generative adversarial network (GAN) is usually built from the centralized, independent identically distributed (i.i.d.) training data to generate realistic-like instances. In real-world applications, however, the data may be distributed over multiple clients and hard to be gathered due to bandw...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 34; H. 12; S. 10502 - 10515
Hauptverfasser:	Li, Wei, Chen, Jinlin, Wang, Zhenyu, Shen, Zhidong, Ma, Chao, Cui, Xiaohui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Collaborative work Computational modeling Convergence Data models Distributed databases Federated learning generative adversarial network (GAN) Generative adversarial networks Machine learning maximum mean discrepancy (MMD) non-independent identically distributed (i.i.d.) training data Training Training data
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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