Fine-grained Distributed Backdoor Attacks in Federated Learning
Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centralized attacks, distributed backdoor attacks are more harmful but require more poisoned samples to compensate for the loss of trigger strength due to decomposition. Fixed trigger patterns are also easily detected by robust aggregation algorithms, increasing the risk of attack exposure. To address these challenges, we propose a fine-grained distributed backdoor attack framework (FDBA). This framework uses dynamic trigger generation and embedding vector optimization to perform attacks with fewer poisoned samples. First, we design a dynamic trig...
| Authors | Jian Wang, Hong Shen, Wei Ke et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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