Papers › DarkFed: A Data-Free Backdoor Attack in Federated Learning

DarkFed: A Data-Free Backdoor Attack in Federated Learning

6 May 2024arXiv:2405.03299links table onlyarchive 2025-07-28

Minghui Li, Wei Wan, Yuxuan Ning, Shengshan Hu, Lulu Xue, Leo Yu Zhang, Yichen Wang

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Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development. To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset. Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed.

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Syntology Ran 6 of 7 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 3 ran · our draft was wrong; 3 ran with no contract checked.

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hustweiwan/darkfed officialmentioned in papermentioned on GitHubpytorch report

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7 samples harvested; 6 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

3ran · our draft was wrong
3ran
1unverified

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ResNetS hustweiwan/DarkFed/models/resnet_s.py official repository ran no licence file found · pointer only · a3c7fa08c0b2a87a · report
conv1x1 hustweiwan/DarkFed/models/resnet.py official repository ran · our draft was wrong no licence file found · pointer only · d9def42110729a85 · report
conv3x3 hustweiwan/DarkFed/models/resnet.py official repository ran · our draft was wrong no licence file found · pointer only · 160bb14bd76201b4 · report
conv3x3 hustweiwan/DarkFed/models/resnet_s.py official repository ran · our draft was wrong no licence file found · pointer only · 583f9780bdd00a45 · report
filter_backdoor hustweiwan/DarkFed/DataFreeTraining.py official repository ran fingerprinted no licence file found · pointer only · e16f961c47753094 · report
predict_the_global_model hustweiwan/DarkFed/DataFreeTraining.py official repository ran no licence file found · pointer only · c6e0e5ce8d72f015 · report
create_bd2 hustweiwan/DarkFed/DataFreeTraining.py official repository unverified no licence file found · pointer only · ec68d3387f37b073 · report

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