FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs-Reference-Cited by-同舟云学术

FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs

Published:2024-08-24 Issue: Volume:1 Page:5070-5081
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Han Shanshan¹,Buyukates Baturalp²,Hu Zijian³,Jin Han²,Jin Weizhao²,Sun Lichao⁴,Wang Xiaoyang⁵,Wu Wenxuan⁶,Xie Chulin⁵,Yao Yuhang⁷,Zhang Kai⁴,Zhang Qifan¹,Zhang Yuhui⁸,Joe-Wong Carlee⁷,Avestimehr Salman²,He Chaoyang³

Affiliation:

1. University of California, Irvine, Irvine, CA, USA

2. University of Southern California, Los Angeles, CA, USA

3. TensorOpera Inc., Palo Alto, CA, USA

4. Lehigh University, Bethlehem, PA, USA

5. UIUC, Urbana, IL, USA

6. Texas A&M University, College Station, TX, USA

7. Carnegie Mellon University, Pittsburgh, PA, USA

8. Zhejiang University, Hangzhou, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671545

Reference111 articles.

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2. Luca Antiga. 2023. Introducing PyTorch Lightning 2.0 and Fabric. https://lightning.ai/blog/introducing-lightning-2-0/ (2023).

3. Federated Learning for Healthcare: Systematic Review and Architecture Proposal

4. Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 2938--2948.

5. Gilad Baruch, Moran Baruch, and Yoav Goldberg. 2019. A little is enough: Circumventing defenses for distributed learning. Advances in Neural Information Processing Systems 32 (2019).

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