arXiv Machine Learning July 21, 2026 By Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Kang Gu, Najrin Sultana, Shagufta Mehnaz

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

Read the original on arXiv Machine Learning →

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.

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

arXiv Machine Learning

Jun 17

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

arXiv:2606. 17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets.

By Md Abdullah Al Mamun, Ngoc Phu Doan, Pedram Zaree, Ihsen Alouani, Nael Abu-Ghazaleh

llms multimodal safety

arXiv AI

Jul 22

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

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By Kareem Amin, Rudrajit Das, Alessandro Epasto, Adel Javanmard, Dennis Kraft, M\'onica Ribero, Sergei Vassilvitskii

llms safety

arXiv Machine Learning

Jun 18

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

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By Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

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arXiv AI

Jun 10

Advancing the State-of-the-Art in Empirical Privacy Auditing

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llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 7

GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning

arXiv:2412. 12640v2 Announce Type: replace Abstract: The increasing demand for data privacy, alongside the benefits of aggregating data from networked devices, has catalyzed the emergence of federated learning (FL).

By Rui Zhang, Ka-Ho Chow

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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

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

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

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GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning

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