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

Abstract

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.