Efficient Privacy-Preserving Federated Learning With Improved Compressed Sensing

Efficient Privacy-Preserving Federated Learning With Improved Compressed Sensing

To solve the data silos issue in distributed machine learning with privacy leakage, privacy-preserving federated learning (PPFL) has been extensively explored in both academic and industrial fields. However, the existing PPFL solutions still suffer from high computation and communication overheads,...

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Bibliographic Details
Main Authors:	ZHANG, Yifan., MIAO, Yinbin., LI, Xinghua., WEI, Linfeng., LIU, Zhiquan, CHOO, Kim-Kwang R., DENG, Robert H.
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2023
Subjects:	CKKS communication costs compression sensing (CS) federated learning (FL) homomorphic encryption Artificial Intelligence and Robotics
Online Access:	https://ink.library.smu.edu.sg/sis_research/8291
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Institution:	Singapore Management University
Language:	English

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