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On the Privacy of Decentralized Machine Learning. (arXiv:2205.08443v1 [cs.CR])
May 18, 2022, 1:20 a.m. | Dario Pasquini, Mathilde Raynal, Carmela Troncoso
cs.CR updates on arXiv.org arxiv.org
In this work, we carry out the first, in-depth, privacy analysis of
Decentralized Learning -- a collaborative machine learning framework aimed at
circumventing the main limitations of federated learning. We identify the
decentralized learning properties that affect users' privacy and we introduce a
suite of novel attacks for both passive and active decentralized adversaries.
We demonstrate that, contrary to what is claimed by decentralized learning
proposers, decentralized learning does not offer any security advantages over
more practical approaches such as …
More from arxiv.org / cs.CR updates on arXiv.org
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