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Privacy-Preserving Distributed Nonnegative Matrix Factorization
March 28, 2024, 4:11 a.m. | Ehsan Lari, Reza Arablouei, Stefan Werner
cs.CR updates on arXiv.org arxiv.org
Abstract: Nonnegative matrix factorization (NMF) is an effective data representation tool with numerous applications in signal processing and machine learning. However, deploying NMF in a decentralized manner over ad-hoc networks introduces privacy concerns due to the conventional approach of sharing raw data among network agents. To address this, we propose a privacy-preserving algorithm for fully-distributed NMF that decomposes a distributed large data matrix into left and right matrix factors while safeguarding each agent's local data privacy. …
address agents applications arxiv cs.cr cs.dc cs.lg data decentralized distributed eess.sp machine machine learning matrix network networks privacy privacy concerns representation sharing signal tool
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