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:2506. 18020v3 Announce Type: replace Abstract: Robust distributed learning algorithms aim to maintain reliable performance despite the presence of misbehaving workers.
By Thomas Boudou, Batiste Le Bars, Nirupam Gupta, Aur\'elien Bellet
The paper introduces a robust decentralized federated distillation approach that allows heterogeneous client models to collaborate using predictions on shared unlabeled public data. Each client evaluates received predictions across three modalities—class prediction, boundary decision, and prediction correlation—filters unreliable clients, assigns reliability-based weights, and constructs modality-specific teachers. The method validates distillation gradients against supervised gradients from private data, removes conflicting gradients, and proves convergence under Byzantine attacks, achieving improved accuracy on CIFAR-10 and CIFAR-100 under non‑IID data and malicious conditions.
By Xiao Ma, Hong Shen, Hui Tian, Wei Ke, Wenqi Lyu
arXiv:2609.36660v1 Announce Type: new
Abstract: We study federated learning (FL) with adversarial clients, where the goal is to minimize the average loss of the honest (non-adversarial) clients witho...
By Leonardo F. Toso, James Anderson, Rafael Pinot, Nirupam Gupta
arXiv:2502.07977v3 Announce Type: replace
Abstract: Empirical risk minimization (ERM) is a cornerstone of modern machine learning. This paper focuses on the man-in-the-middle (MITM) attack, wherein a...
By Cheng Fang, Rishabh Dixit, Waheed U. Bajwa, Mert G\"urb\"uzbalaban
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...