arXiv Machine Learning June 9, 2026 By Yuhang Yao, Jianyi Zhang, Junda Wu, Chengkai Huang, Yu Xia, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Ang Li, Lina Yao, Julian McAuley, Yiran Chen, Carlee Joe-Wong

Federated Large Language Models: Current Progress and Future Directions

Read the original on arXiv Machine Learning →

arXiv:2409. 15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns.

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

arXiv AI

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Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions

arXiv:2608. 13844v1 Announce Type: cross Abstract: Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployment are hindered by high computational costs, data centralization, and privacy concerns.

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llms fine-tuning safety

arXiv Machine Learning

Jun 16

Federated Foundation Language Model Post-Training Should Focus on Open-Source Models

arXiv:2505. 23593v4 Announce Type: replace Abstract: Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and adaptations to user's downstream tasks.

By Nikita Agrawal, Ruben Mayer

llms safety

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 AI

Jun 16

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages

arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.

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arXiv Machine Learning

Jun 3

FederatedSkill: Federated Learning for Agentic Skill Evolution

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arXiv Machine Learning

Jun 3

ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models

arXiv:2511. 19959v2 Announce Type: replace Abstract: Federated learning (FL) has been extensively studied as a privacy-preserving training paradigm.

By Yujia Wang, Yuanpu Cao, Jinghui Chen

llms fine-tuning safety

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Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions

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ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models