Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning-Reference-Cited by-同舟云学术

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning

Published:2024-06-19 Issue: Volume: Page:233-244
ISSN:
Container-title:Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy
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Author:

Kumar K Naveen¹^ORCID,Mohan C Krishna¹^ORCID,Machiry Aravind²^ORCID

Affiliation:

1. Computer Science and Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana, India

2. Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

Funder

National Science Foundation (NSF)

Defense Advanced Research Projects Agency (DARPA)

Amazon Research Award (ARA) on Security Verification and Hardening of CI Workflows

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626232.3653268

Reference45 articles.

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3. Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, and Jeff Bilmes. 2021. Diverse client selection for federated learning via submodular maximization. In International Conference on Learning Representations.

4. Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017. Machine learning with adversaries: Byzantine tolerant gradient descent. Advances in Neural Information Processing Systems , Vol. 30 (2017).

5. André Brand ao, Ricardo Mendes, and Jo ao P Vilela. 2022. Prediction of mobile app privacy preferences with user profiles via federated learning. In Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy. 89--100.