A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning Article Swipe
Anqi Zhou
,
Yezheng Liu
,
Yidong Chai
,
Hongyi Zhu
,
Xinyue Ge
,
Yuanchun Jiang
,
Meng Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.00719
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.00719
Federated Learning (FL) has garnered widespread adoption across various domains such as finance, healthcare, and cybersecurity. Nonetheless, FL remains under significant threat from backdoor attacks, wherein malicious actors insert triggers into trained models, enabling them to perform certain tasks while still meeting FL's primary objectives. In response, robust aggregation methods have been proposed, which can be divided into three types: ex-ante, ex-durante, and ex-post methods. Given the complementary nature of these methods, combining all three types is promising yet unexplored. Such a combination is non-trivial because it requires leveraging their advantages while overcoming their disadvantages. Our study proposes a novel whole-process certifiably robust aggregation (WPCRA) method for FL, which enhances robustness against backdoor attacks across three phases: ex-ante, ex-durante, and ex-post. Moreover, since the current geometric median estimation method fails to consider differences among clients, we propose a novel weighted geometric median estimation algorithm (WGME). This algorithm estimates the geometric median of model updates from clients based on each client's weight, further improving the robustness of WPCRA against backdoor attacks. We also theoretically prove that WPCRA offers improved certified robustness guarantees with a larger certified radius. We evaluate the advantages of our methods based on the task of loan status prediction. Comparison with baselines shows that our methods significantly improve FL's robustness against backdoor attacks. This study contributes to the literature with a novel WPCRA method and a novel WGME algorithm. Our code is available at https://github.com/brick-brick/WPCRAM.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.00719
- https://arxiv.org/pdf/2407.00719
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400341291
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4400341291Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.00719Digital Object Identifier
- Title
-
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-30Full publication date if available
- Authors
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Anqi Zhou, Yezheng Liu, Yidong Chai, Hongyi Zhu, Xinyue Ge, Yuanchun Jiang, Meng WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.00719Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2407.00719Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2407.00719Direct OA link when available
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Backdoor, Process (computing), Computer science, Artificial intelligence, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.theoretically | 172 |
| abstract_inverted_index.whole-process | 100 |
| abstract_inverted_index.cybersecurity. | 15 |
| abstract_inverted_index.disadvantages. | 94 |
| abstract_inverted_index.https://github.com/brick-brick/WPCRAM. | 236 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |