arXiv:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
By Hongliang Zhang, Zhongyuan Yu, Fenghua Xu, Teng Hu, Jian Meng, Jiguo Yu
arXiv:2609.15521v1 Announce Type: new
Abstract: Federated learning enables multiple parties to train a shared model without centralizing raw data with the help of an aggregator, but introduces integr...
By Doryan Lesaignoux, Enrique M\'armol Campos, Gabriele Spini, Jos\'e L. Hern\'andez-Ramos, Stephan Krenn
arXiv:2609.37011v1 Announce Type: cross
Abstract: Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into...
By Hongxu Su, Jianzhu Yao, Xuechao Wang, Pramod Viswanath
The paper investigates how privacy guarantees, robustness to Byzantine attacks, and detection coverage for rare intrusion types interact in federated network intrusion detection systems. It introduces geometric indistinguishability to explain how privacy noise can obscure minority-class signals, and demonstrates through experiments on UNSW‑NB15 that combining differential privacy with robust aggregation can disproportionately harm detection of rare attacks. The study highlights that these properties cannot be treated as independently composable and calls for aggregation‑aware modeling and sample‑aware evaluation to build trustworthy federated NIDS.
By Adrita Rahman Tory, ABM Shawkat Ali, Md Abu Layek, Khondokar Fida Hasan
The paper investigates how multi‑step tool‑calling large language model agents can unintentionally create persistent billable state, allowing malicious or compromised tools to generate repeated charges without user credentials. It formalizes the persistent billable‑state boundary, identifies six denial‑of‑wallet attack vectors, and evaluates them with the DOW‑BENCH harness across six model families. The study shows significant cost amplification, demonstrates effective mitigation via deterministic history transformation and host‑side invariants, and highlights the scarcity of existing safeguards in real‑world repositories.
By Jinqian Zhang (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences), Haojun Xia (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences), Shujiang Wu (Beihang University), Jingkun Yue (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China), Xia Zhang (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences), Zhangpei Cheng (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences), Bibo Tu (Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences)
The paper proposes using lossy compression as a security signal in federated learning by defining a "compression footprint"—a low‑dimensional set of statistics derived from the compressor’s output. It introduces CRAFT, a server‑side aggregation method that leverages these footprints to distinguish honest from malicious updates without extra client metadata or knowledge of attacker numbers. Experiments on six attacks and datasets show CRAFT often outperforms existing robust aggregation baselines, demonstrating that compression can simultaneously reduce communication and enhance security.
By Sachi Shome, William Eiers