Defending poisoning attacks in federated learning via adversarial training method

Paper

Zhang, Jiale, Wu, Di, Liu, Chengyong and Chen, Bing. 2020. "Defending poisoning attacks in federated learning via adversarial training method." 3rd International Conference on Frontiers in Cyber Security (FCS 2020). Tianjin, China 15 - 17 Nov 2020 Singapore . Springer. https://doi.org/10.1007/978-981-15-9739-8_7
Paper/Presentation Title

Defending poisoning attacks in federated learning via adversarial training method

Presentation TypePaper
AuthorsZhang, Jiale, Wu, Di, Liu, Chengyong and Chen, Bing
Journal or Proceedings TitleProceedings of the 3rd International Conference on Frontiers in Cyber Security (FCS 2020)
Journal Citationpp. 83-94
Number of Pages12
Year2020
PublisherSpringer
Place of PublicationSingapore
ISBN9789811597381
9789811597398
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-15-9739-8_7
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-981-15-9739-8_7
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-981-15-9739-8
Conference/Event3rd International Conference on Frontiers in Cyber Security (FCS 2020)
Event Details
3rd International Conference on Frontiers in Cyber Security (FCS 2020)
Delivery
In person
Event Date
15 to end of 17 Nov 2020
Event Location
Tianjin, China
Abstract

Recently, federated learning has shown its significant advantages in protecting training data privacy by maintaining a joint model across multiple clients. However, its model security issues have not only been recently explored but shown that federated learning exhibits inherent vulnerabilities on the active attacks launched by malicious participants. Poisoning is one of the most powerful active attacks where an inside attacker can upload the crafted local model updates to further impact the global model performance. In this paper, we first illustrate how the poisoning attack works in the context of federated learning. Then, we correspondingly propose a defense method that mainly relies upon a well-researched adversarial training technique: pivotal training, which improves the robustness of the global model with poisoned local updates. The main contribution of this work is that the countermeasure method is simple and scalable since it does not require complex accuracy validations, while only changing the optimization objectives and loss functions. We finally demonstrate the effectiveness of our proposed mitigation mechanisms through extensive experiments.

KeywordsFederated learning; Poisoning attacks; Label-flipping; Pivotal training
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Public Notes

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Byline AffiliationsNanjing University of Aeronautics and Astronautics, China
University of Technology Sydney
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