arXiv AI July 20, 2026 By Soumia Zohra El Mestari, Maciej Krzysztof Zuziak, Gabriele Lenzini

Poison to Detect: Detection of Targeted Overfitting in Federated Learning

arXiv:2509. 11974v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET).

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

safety

arXiv Machine Learning

Jun 18

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.

By Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

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

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

arXiv Machine Learning

Jul 21

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

arXiv:2310. 16152v5 Announce Type: replace-cross Abstract: Federated learning (FL) has become a key component in various language modeling applications such as machine translation, next-word prediction, and medical record analysis.

By Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Kang Gu, Najrin Sultana, Shagufta Mehnaz

llms nlp safety

arXiv Machine Learning

5d ago

Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice

arXiv:2608. 12962v1 Announce Type: new Abstract: Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models.

By Ziqi Zhao, Jialin Lu, Junjie Shan, Junyuan Zhang, Shuya Yang, Ka-Ho Chow

benchmarks safety

arXiv AI

Jun 2

Safe-FedLLM: Delving into the Safety of Federated Large Language Models

arXiv:2601. 07177v5 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs).

By Mingxiang Tao, Yu Tian, Wenxuan Tu, Yue Yang, Xue Yang, Xiangyan Tang

llms fine-tuning safety

Related stories

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

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

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

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice

Safe-FedLLM: Delving into the Safety of Federated Large Language Models