PDGAN: A novel poisoning defense method in federated learning using generative adversarial network

Paper

Zhao, Ying, Chen, Junjun, Zhang, Jiale, Wu, Di, Teng, Jian and Yu, Shui. 2024. "PDGAN: A novel poisoning defense method in federated learning using generative adversarial network." 19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019). Melbourne, Australia 09 - 11 Dec 2019 Switzerland. Springer. https://doi.org/10.1007/978-3-030-38991-8_39

Paper/Presentation Title	PDGAN: A novel poisoning defense method in federated learning using generative adversarial network
Presentation Type	Paper
Authors	Zhao, Ying, Chen, Junjun, Zhang, Jiale, Wu, Di, Teng, Jian and Yu, Shui
Journal or Proceedings Title	Proceedings of the 19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019)
Journal Citation	11944, pp. 595-609
Number of Pages	15
Year	2024
Publisher	Springer
Place of Publication	Switzerland
ISBN	9783030389901
	9783030389918
Digital Object Identifier (DOI)	https://doi.org/10.1007/978-3-030-38991-8_39
Web Address (URL) of Paper	https://link.springer.com/chapter/10.1007/978-3-030-38991-8_39
Web Address (URL) of Conference Proceedings	https://link.springer.com/book/10.1007/978-3-030-38991-8
Conference/Event	19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019)
Event Details	19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019) Parent International Conference on Algorithms and Architectures for Parallel Processing Delivery In person Event Date 09 to end of 11 Dec 2019 Event Location Melbourne, Australia
Abstract	Federated learning can complete an enormous training task efficiently by inviting participants to train a deep learning model collaboratively, and the user privacy will be well preserved for the users only upload model parameters to the centralized server. However, the attackers can initiate poisoning attacks by uploading malicious updates in federated learning. Therefore, the accuracy of the global model will be impacted significantly after the attack. To address this vulnerability, we propose a novel poisoning defense generative adversarial network (PDGAN) to defend the poising attack. The PDGAN can reconstruct training data from model updates and audit the accuracy for each participant model by using the generated data. Precisely, the participant whose accuracy is lower than a predefined threshold will be identified as an attacker and model parameters of the attacker will be removed from the training procedure in this iteration. Experiments conducted on MNIST and Fashion-MNIST datasets demonstrate that our approach can indeed defend the poisoning attacks in federated learning.
Keywords	Federated learning; Poisoning defense; Generative adversarial network
Contains Sensitive Content	Does not contain sensitive content
ANZSRC Field of Research 2020	4604. Cybersecurity and privacy
	4602. Artificial intelligence
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Series	Lecture Notes in Computer Science
Byline Affiliations	Beijing University of Chemical Technology, China
	Nanjing University of Aeronautics and Astronautics, China
	University of Technology Sydney

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