FedLNS is a server‑side framework that screens federated learning updates by representing each client’s contribution through changes in trainable normalization‑layer parameters, creating lightweight signatures that can be compared against a history‑aware cross‑client reference. The method requires no extra client‑to‑server communication, raw data, or labeled attack examples, and after screening, the remaining full‑model updates are aggregated with standard federated learning rules. Experiments on GPT‑style, BERT‑style, and LLaMA‑style models with 200 clients demonstrate that FedLNS achieves lower test perplexity than six baselines even when 40% of the population performs target manipulation under both IID and non‑IID data partitions.
By Kai Li, Jong-Ik Park, Carlee Joe-Wong, Wei Ni, Falko Dressler
arXiv:2609.07147v1 Announce Type: new
Abstract: Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to central...
By Jian Wang, Hong Shen, Wei Ke, Xue Hua Liu
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.
By Jian Wang, Hong Shen, Chan-Tong Lam
FedNIA is a defense framework for federated learning that identifies and excludes malicious clients without needing a central test dataset. It works by injecting random noise inputs and analyzing layerwise activation patterns with an autoencoder to detect abnormal behaviors caused by data poisoning. The method can counter various attack types—including sample poisoning, label flipping, and backdoors—even when multiple attackers collaborate, and shows strong performance on non‑iid federated datasets.
By Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
arXiv:2508. 04064v2 Announce Type: replace-cross Abstract: Horizontal federated learning (HFL) backdoor audits often summarize model behavior through clean accuracy (CA), mean attack success rate (ASR), or a single known-trigger test.
By Tuan Nguyen, Sze Jue Yang, Khoa D. Doan, Chee Seng Chan, Kok-Seng Wong
The paper introduces STAIN-FL, a stealthy backdoor attack framework for federated video anomaly detection that uses natural surveillance conditions—such as low light, indoor settings, and crowd density—as contextual triggers. STAIN-FL manipulates anomaly labels and masks gradients to keep clean accuracy low while inducing trigger‑conditioned misclassification. Experiments on UCF‑Crime with I3D features show that sparse attacks remain undetectable, drop clean accuracy by less than 2%, yet achieve over 50% backdoor accuracy for hundreds of rounds under FedAvg and FedProx.
By Ashlinder Kaur, Purnima Murali Mohan, Zengxiang Li, Tram Truong-Huu