Adaptive vertical federated learning via feature map transferring in mobile edge computing

To bring more intelligence to edge systems, Federated Learning (FL) is proposed to provide a privacy-preserving mechanism to train a globally shared model by utilizing a massive amount of user-generated data on devices. FL enables multiple clients collaboratively train a machine learning model while...

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Veröffentlicht in:	Computing Jg. 106; H. 4; S. 1081 - 1097
Hauptverfasser:	Li, Yuanzhang, Sha, Tianchi, Baker, Thar, Yu, Xiao, Shi, Zhiwei, Hu, Sikang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vienna Springer Vienna 01.04.2024 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Artificial neural networks Clients Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Datasets Edge computing Feature extraction Feature maps Federated learning Image classification Information Systems Applications (incl.Internet) Iterative methods Machine learning Mobile computing Representations Software Engineering Special Issue Article Federated learning 68T05 Transfer learning Convolutional neural network Machine learning
ISSN:	0010-485X, 1436-5057
Online-Zugang:	Volltext
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