arXiv:2607. 07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself.
By Chongkai Li, Bang Zhang, Wenjian Luo
arXiv:2601.00900v2 Announce Type: replace-cross
Abstract: As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target re...
By Yuchao Hou (Shanxi Normal University, Taiyuan, China), Zixuan Zhang (Shanxi Normal University, Taiyuan, China), Jie Wang (Shanxi Normal University, Taiyuan, China), Wenke Huang (Nanyang Technological University, Singapore, Singapore), Lianhui Liang (Guangxi University, Nanning, China), Di Wu (La Trobe University, Melbourne, Australia), Zhiquan Liu (Jinan University, Guangzhou, China), Youliang Tian (Guizhou University, Guiyang, China), Jianming Zhu (Central University of Finance and Economics, Beijing, China), Jisheng Dang (Lanzhou University, Lanzhou, China), Junhao Dong (Nanyang Technological University, Singapore, Singapore), Zhongliang Guo (University of St Andrews, St Andrews, United Kingdom)
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
Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself. Recently, centralized Taking Away Training Data (TATD) attacks have shown that malicious training could abuse the memorization capacity of deep models to store and later recover training data.
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 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