Lightweight privacy-preserving cross-cluster federated learning with heterogeneous data

Lightweight privacy-preserving cross-cluster federated learning with heterogeneous data

Federated Learning (FL) eliminates data silos that hinder digital transformation while training a shared global model collaboratively. However, training a global model in the context of FL has been highly susceptible to heterogeneity and privacy concerns due to discrepancies in data distribution, wh...

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書目詳細資料
Main Authors:	CHEN, Zekai, YU, Shengxing, CHEN, Farong, WANG, Fuyi, LIU, Ximeng, DENG, Robert H.
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2024
主題:	Distributed machine learning federated learning heterogeneity data privacy preserving Artificial Intelligence and Robotics Information Security
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/9637
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

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