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arXiv AI By Jian Wang, Hong Shen, Chan-Tong Lam

Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learning

Read the original on arXiv AI →

The paper introduces Fractal-Triggered Distributed Backdoor Attack (FTDBA), a new method that uses fractal self‑similarity to strengthen sub‑triggers in federated learning backdoor attacks. By employing a dynamic angular perturbation mechanism, FTDBA reduces the amount of poisoned data needed while maintaining a 92.3% attack success rate. Experiments show a 22.8% lower detection rate and a 41.2% reduction in KL divergence compared to traditional distributed backdoor attacks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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