The paper investigates how adversarial examples transfer between client models in federated learning and explores the relationship between these examples and client data distributions. It proposes a defense strategy based on adversarial training that leverages the transferability of model robustness. Experiments on real-life datasets demonstrate that the new attack and defense methods outperform existing state‑of‑the‑art approaches.
By Zuobin Xiong, Deval Mukherjee, Homook Cho, Wei Li
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
The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of machine learning models. However, the involvement of a...
arXiv:2608. 12962v1 Announce Type: new Abstract: Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models.
By Ziqi Zhao, Jialin Lu, Junjie Shan, Junyuan Zhang, Shuya Yang, Ka-Ho Chow
The paper investigates how the strategic placement of Byzantine nodes in decentralized federated learning (DFL) affects the propagation of malicious influence across the communication graph. It introduces Byzantine Placement Influence (BPI), a measure that captures cumulative exposure of honest nodes to Byzantine sources over time, and develops algorithms to optimize BPI across various network structures and attack types. Experiments demonstrate that BPI-guided placements consistently yield highly damaging configurations, highlighting the importance of considering node placement in DFL threat models.
By Edoardo Gabrielli, Gabriele Tolomei
arXiv:2608. 06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior.
By Srinivasan Subramanian, Md. Abdullah Al Hafiz Khan, Kazi Aminul Islam
arXiv:2509. 11974v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET).
By Soumia Zohra El Mestari, Maciej Krzysztof Zuziak, Gabriele Lenzini
The paper introduces Fed-ADR, a coordinated attack framework where a malicious orchestrator server directs heterogeneous adversarial clients to adapt their gradient updates in real time, thereby evading existing federated learning defenses and drastically reducing global model accuracy. It also presents a lightweight detection mechanism that estimates true client gradients from historical data to spot coordinated attacks, and an in-situ recovery method that restores model performance without restarting training. Experiments on MNIST, Fashion‑MNIST, and CIFAR‑10 show the attack can drop accuracy from over 90% to below 10%, while the defense can recover accuracy to above 90% within a few rounds at a computational cost at least 20× lower than retraining from scratch.
By Mohamed Shaaban, Ahmed Abdelnaby, Mohamed Elmahallawy
arXiv:2610.20264v1 Announce Type: new
Abstract: Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or tilted empirical risk minim...
By Tiantong Sun, Jinlin Liu, Xingru Wei, Zequan Fang, Wei Chen, Qiang Yang, Xiaojin Zhang
arXiv:2606. 17035v1 Announce Type: new Abstract: Prior research suggests that differential privacy (DP) inherently enhances the robustness of federated learning (FL) against backdoor attacks.
By Xiaolin Li, Ning Wang, Ninghui Li, Wenhai Sun
The paper proposes Byrd-NAFL, a Byzantine‑robust federated learning algorithm that incorporates Nesterov’s momentum and resilient aggregation rules. It achieves fast and safe convergence under non‑convex, smooth loss functions with relaxed gradient assumptions, and provides a finite‑time convergence guarantee. Experiments show that Byrd-NAFL outperforms existing methods in convergence speed, accuracy, and resilience to various malicious attacks.
By Lihan Xu, Xiaoyi Fan, Gang Wang, Runhao Zeng, Xiping Hu, Yanjie Dong
arXiv:2410. 11378v3 Announce Type: replace-cross Abstract: Personalized collaborative learning in federated settings faces a critical trade-off between customization and participant trust.
By Yawen Li, Yan Li, Junping Du, Yingxia Shao, Meiyu Liang, Guanhua Ye