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This content will become publicly available on December 1, 2024

Title: Lockdown: backdoor defense for federated learning with isolated subspace training
Federated learning (FL) is vulnerable to backdoor attacks due to its distributed computing nature. Existing defense solution usually requires larger amount of computation in either the training or testing phase, which limits their practicality in the resource-constrain scenarios. A more practical defense, i.e., neural network (NN) pruning based defense has been proposed in centralized backdoor setting. However, our empirical study shows that traditional pruning-based solution suffers poison-coupling effect in FL, which significantly degrades the defense performance. This paper presents Lockdown, an isolated subspace training method to mitigate the poison-coupling effect. Lockdown follows three key procedures. First, it modifies the training protocol by isolating the training subspaces for different clients. Second, it utilizes randomness in initializing isolated subspacess, and performs subspace pruning and subspace recovery to segregate the subspaces between malicious and benign clients. Third, it introduces quorum consensus to cure the global model by purging malicious/dummy parameters. Empirical results show that Lockdown achieves superior and consistent defense performance compared to existing representative approaches against backdoor attacks. Another value-added property of Lockdown is the communication-efficiency and model complexity reduction, which are both critical for resource-constrain FL scenario. Our code is available at https://github.com/git-disl/Lockdown.  more » « less
Award ID(s):
2312758
NSF-PAR ID:
10515961
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the 37th International Conference on Neural Information Processing Systems
Volume:
478
ISSN:
1049-5258
ISBN:
978-1-7138-2954-6
Page Range / eLocation ID:
10876–10896
Format(s):
Medium: X
Location:
New Orleans, LA USA
Sponsoring Org:
National Science Foundation
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