Privacy-preserving distributed projection LMS for linear multitask networks

• Main Authors: Wang, Chengcheng, Tay, Wee Peng, Wei, Ye, Wang, Yuan
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Computer science and engineering; Engineering::Electrical and electronic engineering::Electronic systems::Signal processing; Distributed Strategies; Multitask Networks; Inference Privacy; Privacy Preservation; Additive Noises
• 在線閱讀: https://hdl.handle.net/10356/156347
• 機構: Nanyang Technological University

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...