Privacy-preserving distributed projection LMS for linear multitask networks

Privacy-preserving distributed projection LMS for linear multitask networks

We develop a privacy-preserving distributed projection least mean squares (LMS) strategy over linear multitask networks, where agents' local parameters of interest or tasks are linearly related. Each agent is interested in not only improving its local inference performance via in-network cooper...

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Bibliographic Details
Main Authors: Wang, Chengcheng, Tay, Wee Peng, Wei, Ye, Wang, Yuan
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Distributed Strategies
Multitask Networks
Inference Privacy
Privacy Preservation
Additive Noises
Online Access:https://hdl.handle.net/10356/156347

Internet

https://hdl.handle.net/10356/156347

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