Going Haywire: False Friends in Federated Learning and How to Find Them

Author:

Aiken William1ORCID,Branco Paula1ORCID,Jourdan Guy-Vincent1ORCID

Affiliation:

1. School of Electrical Engineering and Computer Science, University of Ottawa, Canada

Funder

IBM Center for Advanced Studies (CAS) Canada

Publisher

ACM

Reference44 articles.

1. Yossi Adi , Carsten Baum , Moustapha Cisse , Benny Pinkas , and Joseph Keshet . 2018 . Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring . In 27th USENIX Security Symposium (USENIX Security 18) . USENIX Association, Baltimore, MD, 1615–1631. https://www.usenix.org/conference/usenixsecurity18/presentation/adi Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. 2018. Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring. In 27th USENIX Security Symposium (USENIX Security 18). USENIX Association, Baltimore, MD, 1615–1631. https://www.usenix.org/conference/usenixsecurity18/presentation/adi

2. William Aiken , Hyoungshick Kim , Simon Woo , and Jungwoo Ryoo . 2021. Neural network laundering: Removing black-box backdoor watermarks from deep neural networks. Computers & Security 106 ( 2021 ). https://doi.org/10.1016/j.cose.2021.102277 10.1016/j.cose.2021.102277 William Aiken, Hyoungshick Kim, Simon Woo, and Jungwoo Ryoo. 2021. Neural network laundering: Removing black-box backdoor watermarks from deep neural networks. Computers & Security 106 (2021). https://doi.org/10.1016/j.cose.2021.102277

3. Manoj Ghuhan Arivazhagan , Vinay Aggarwal , Aaditya Kumar Singh , and Sunav Choudhary . 2019. Federated Learning with Personalization Layers. arXiv preprint ( 2019 ). https://doi.org/10.48550/arXiv.1912.00818 10.48550/arXiv.1912.00818 Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019. Federated Learning with Personalization Layers. arXiv preprint (2019). https://doi.org/10.48550/arXiv.1912.00818

4. Sana Awan , Bo Luo , and Fengjun Li . 2021 . CONTRA: Defending against poisoning attacks in federated learning . In European Symposium on Research in Computer Security. Springer, 455–475 . https://doi.org/10.1007/978-3-030-88418-5_22 10.1007/978-3-030-88418-5_22 Sana Awan, Bo Luo, and Fengjun Li. 2021. CONTRA: Defending against poisoning attacks in federated learning. In European Symposium on Research in Computer Security. Springer, 455–475. https://doi.org/10.1007/978-3-030-88418-5_22

5. Franziska Boenisch , Adam Dziedzic , Roei Schuster , Ali Shahin Shamsabadi , Ilia Shumailov , and Nicolas Papernot . 2021. When the curious abandon honesty: Federated learning is not private. arXiv preprint ( 2021 ). https://doi.org/10.48550/arXiv.2112.02918 10.48550/arXiv.2112.02918 Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot. 2021. When the curious abandon honesty: Federated learning is not private. arXiv preprint (2021). https://doi.org/10.48550/arXiv.2112.02918

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