arXiv Machine Learning August 28, 2026 By Ahmed Aldahdooh, Wassim Hamidouche, Olivier D\'eforges

Federated Adversarial Training with Transformers

Read the original on arXiv Machine Learning →

The paper investigates federated adversarial training (AT) for vision transformers, a topic not previously explored in federated learning (FL). It evaluates various transformer architectures and aggregation strategies, and introduces FedWAvg, an extension of FedAvg that weights client updates based on similarity of their last-layer representations. Experiments demonstrate that FedWAvg yields higher robust accuracy than existing aggregation methods in non‑IID settings.

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.

arXiv AI

Jun 30

AEGIS: A Semantic GAN and Evidential Learning Frameworkfor Robust Adversarial Detection in Vision Sensors

arXiv:2606. 28416v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions.

By Maher Boughdiri, Mounira Msahli, Albert Bifet

diffusion computer-vision safety

arXiv Machine Learning

Jul 13

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers

arXiv:2607. 09480v1 Announce Type: cross Abstract: The human visual system (HVS) employs foveated sampling and eye movements to achieve efficient perception, conserving both metabolic energy and computational resources.

By Ibrahim Batuhan Akkaya, Kishaan Jeeveswaran, Bahram Zonooz, Elahe Arani

llms rag computer-vision safety

arXiv Machine Learning

Jun 2

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.

By Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati, Pau Vilimelis Aceituno

benchmarks safety

arXiv Machine Learning

Jul 7

Binary Iterative Method for Non-targeted Adversarial Attack

arXiv:2607. 04145v1 Announce Type: new Abstract: Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expose the 'piecewise linearity' of deep learning based models.

By Naman Goyal, Milan Chaudhari

safety

arXiv AI

Jun 26

Byzantine-Robust Aggregation for Securing Decentralized Federated Learning

arXiv:2409. 17754v2 Announce Type: replace-cross Abstract: Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices.

By Diego Cajaraville-Aboy, Ana Fern\'andez-Vilas, Rebeca P. D\'iaz-Redondo, Manuel Fern\'andez-Veiga

computer-vision benchmarks safety

arXiv AI

Jul 14

Enhancing Adversarial Transferability through Block Stretch and Shrink

arXiv:2511. 17688v2 Announce Type: replace-cross Abstract: Input transformation-based attacks improve adversarial transferability by aggregating gradients over transformed inputs.

By Quan Liu, Feng Ye, Chenhao Lu, Shuming Zhen, Guanliang Huang, Lunzhe Chen, Xudong Ke

llms computer-vision safety

One email a morning, machine-written

Related stories

AEGIS: A Semantic GAN and Evidential Learning Frameworkfor Robust Adversarial Detection in Vision Sensors

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

Binary Iterative Method for Non-targeted Adversarial Attack

Byzantine-Robust Aggregation for Securing Decentralized Federated Learning

Enhancing Adversarial Transferability through Block Stretch and Shrink