3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning

Haoyang Li, Qingqing Ye, Haibo Hu, Jin Li, Leixia Wang, Chengfang Fang, Jie Shi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


Federated Learning (FL), the de-facto distributed machine learning paradigm that locally trains datasets at individual devices, is vulnerable to backdoor model poisoning attacks. By compromising or impersonating those devices, an attacker can upload crafted malicious model updates to manipulate the global model with backdoor behavior upon attacker-specified triggers. However, existing backdoor attacks require more information on the victim FL system beyond a practical black-box setting. Furthermore, they are often specialized to optimize for a single objective, which becomes ineffective as modern FL systems tend to adopt in-depth defense that detects backdoor models from different perspectives. Motivated by these concerns, in this paper, we propose 3DFed, an adaptive, extensible, and multi-layered framework to launch covert FL backdoor attacks in a black-box setting. 3DFed sports three evasion modules that camouflage backdoor models: backdoor training with constrained loss, noise mask, and decoy model. By implanting indicators into a backdoor model, 3DFed can obtain the attack feedback in the previous epoch from the global model and dynamically adjust the hyper-parameters of these backdoor evasion modules. Through extensive experimental results, we show that when all its components work together, 3DFed can evade the detection of all state-of-the-art FL backdoor defenses, including Deepsight, Foolsgold, FLAME, FL-Detector, and RFLBAT. New evasion modules can also be incorporated in 3DFed in the future as it is an extensible framework.

Original languageEnglish
Title of host publicationProceedings - 44th IEEE Symposium on Security and Privacy, SP 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages15
ISBN (Electronic)9781665493369
Publication statusPublished - Jul 2023
Event44th IEEE Symposium on Security and Privacy, SP 2023 - Hybrid, San Francisco, United States
Duration: 22 May 202325 May 2023

Publication series

NameProceedings - IEEE Symposium on Security and Privacy
ISSN (Print)1081-6011


Conference44th IEEE Symposium on Security and Privacy, SP 2023
Country/TerritoryUnited States
CityHybrid, San Francisco

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Software
  • Computer Networks and Communications


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