arXiv Machine Learning
Sep 23

On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression

The paper studies federated learning where honest clients have heterogeneous data-generating models and adversarial clients can exacerbate this heterogeneity by sending arbitrary updates. It derives new bounds on gradient heterogeneity for linear and nonlinear regression, separating effects from honest clients’ model differences, label noise, and initialization. The authors show that for any (f,κ)-robust aggregator with κ = O(f/n) (where f is the number of adversarial clients and n the total number of clients, with f/n < 1/2), convergence is guaranteed after an explicit sample burn‑in period.

By Leonardo F. Toso, James Anderson, Nirupam Gupta, Rafael Pinot
arXiv Machine Learning
Aug 27

Rethinking the Transferable Adversarial Attacks and Robust Defense in Federated Learning

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