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arXiv Machine Learning By Chao Feng, Burkhard Stiller

CACTUS: Mask-Guided Semantic Clean-Label Backdoors in Decentralized Federated Learning

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CACTUS is a new backdoor attack for decentralized federated learning that uses mask‑guided, modality‑specific operators to convert label‑consistent semantic pairs into target‑directed representation shifts. By isolating trigger effects and applying them counterfactually to clean embeddings before peer aggregation, CACTUS can propagate backdoors across repeated aggregation rounds. Experiments on speech, text, tabular, and image tasks show that with 30% malicious nodes, CACTUS achieves a mean attack success rate of 51.2% on Speech Commands and the highest mean ASR among evaluated attacks on three of four modalities, with success varying by network topology and malicious‑node ratio.

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