← Back to all news
arXiv Machine Learning By Zuobin Xiong, Deval Mukherjee, Homook Cho, Wei Li

Rethinking the Transferable Adversarial Attacks and Robust Defense in Federated Learning

Read the original on arXiv Machine 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 23

FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning

FedNIA is a defense framework for federated learning that identifies and excludes malicious clients without needing a central test dataset. It works by injecting random noise inputs and analyzing layerwise activation patterns with an autoencoder to detect abnormal behaviors caused by data poisoning. The method can counter various attack types—including sample poisoning, label flipping, and backdoors—even when multiple attackers collaborate, and shows strong performance on non‑iid federated datasets.

By Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
safety
More like this →