4.3 Article

A Taxonomy of Attacks on Federated Learning

Journal

IEEE SECURITY & PRIVACY
Volume 19, Issue 2, Pages 20-28

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/MSEC.2020.3039941

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Funding

  1. Semiconductor Research Corporation

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Federated learning is a privacy-focused framework for training deep neural networks from decentralized data sources, but it is vulnerable to numerous attacks. A more robust threat modeling is needed to enhance security in federated learning environments.
Federated learning is a privacy-by-design framework that enables training deep neural networks from decentralized sources of data, but it is fraught with innumerable attack surfaces. We provide a taxonomy of recent attacks on federated learning systems and detail the need for more robust threat modeling in federated learning environments.

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