CASBA: Capability-Adaptive Shadow Backdoor Attack against Federated Learning

Federated Learning (FL) protects data privacy through a distributed training mechanism, yet its decentralized nature also introduces new security vulnerabilities. Backdoor attacks inject malicious triggers into the global model through compromised updates, posing significant threats to model integrity and becoming a key focus in FL security. Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity, resulting in limited stealth and adaptability. To address the heterogeneity of malicious client devices, this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack (CASBA). By incorporating measurements of clients’ computational and communication capabilities, CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources. Furthermore, an improved deep convolutional generative adversarial network (DCGAN) is integrated into the attack pipeline to embed triggers without modifying original data, significantly enhancing stealthiness. Comparative experiments with Shadow Backdoor Attack (SBA) across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities, reducing average memory usage per iteration by 5.8%. CASBA improves resource efficiency while keeping the drop in attack success rate within 3%. Additionally, the effectiveness of CASBA against three robust FL algorithms is also validated.