Adversarial client detection via non-parametric subspace monitoring in the internet of federated things-Reference-Cited by-同舟云学术

Adversarial client detection via non-parametric subspace monitoring in the internet of federated things

Published:2024-07-29 Issue: Volume: Page:1-13
ISSN:2472-5854
Container-title:IISE Transactions
language:en
Short-container-title:IISE Transactions

Author:

Xie Xianjian¹,Xian Xiaochen²,Li Dan³,Wang Andi³^ORCID

Affiliation:

1. School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA

2. School of Industrial and Systems Engineering, Georgia Institute of Technology, GA, USA

3. Department of Industrial and Systems Engineering, the University of Wisconsin-Madison, Madison, WI, USA

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725854.2024.2367224

Reference45 articles.

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3. Azad, K.M.S., Hossain, N., Islam, M.J., Rahman, A. and Kabir, S. (2021) Preventive determination and avoidance of DDOS attack with SDN over the IOT networks, in 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), IEEE Press, Piscataway, NJ, pp. 1–6.

4. Azam, S.S., Hosseinalipour, S., Qiu, Q. and Brinton, C. (2021) Recycling model updates in federated learning: Are gradient subspaces low-rank? in International Conference on Learning Representations, OpenReview.net, Amherst, MA, USA.

5. Bhagoji, A.N., Chakraborty, S., Mittal, P. and Calo, S. (2019) Analyzing federated learning through an adversarial lens, in International Conference on Machine Learning, Proceedings of Machine Learning Research (PMLR), Cambridge, MA, USA, pp. 634–643.