arXiv Machine Learning July 30, 2026 By Hongliang Zhang, Zhongyuan Yu, Guijuan Wang, Tianqing He, Wenshuo Ma, Xiaosong Zhang, Jiguo Yu

Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 26933v1 Announce Type: cross Abstract: Federated Learning (FL) is vulnerable to backdoor attacks because of its distributed nature in edge computing scenarios.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv AI

Aug 10

Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning

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

benchmarks safety

arXiv AI

Jun 9

Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips

arXiv:2606. 09548v1 Announce Type: cross Abstract: Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data.

By Bastien Vuillod, Kevin Hector, Pierre-Alain Moellic, Jean-Max Dutertre, Olivier Potin

arXiv AI

Jun 3

Patcher: Post-Hoc Patching of Backdoored Large Language Models

arXiv:2606. 02995v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak backdoor attacks, where adversaries poison safety alignment data to embed hidden triggers that bypass safety mechanisms.

By Anjun Gao, Yueyang Quan, Yufei Xia, Zhuqing Liu, Minghong Fang

llms fine-tuning safety

arXiv Machine Learning

Jun 16

Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning

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

benchmarks safety

arXiv Machine Learning

Jul 13

Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment

arXiv:2601. 22313v2 Announce Type: replace Abstract: Large Language Models (LLMs) are rarely static and are frequently updated in practice.

By Yavuz Bakman, Duygu Nur Yaldiz, Eleni Triantafillou, Peter Kairouz, Salman Avestimehr, Sai Praneeth Karimireddy

llms fine-tuning safety

arXiv Machine Learning

Aug 4

FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices

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

Related stories

Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning

Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips

Patcher: Post-Hoc Patching of Backdoored Large Language Models

Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning

Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment

FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices