Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration Article Swipe

MAKE IMPACT

Xuhan Zuo , Minghao Wang , Tianqing Zhu , Shui Yu , Wanlei Zhou · YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2412.13551

Large language models (LLMs) have transformed the way computers understand and process human language, but using them effectively across different organizations remains still difficult. When organizations work together to improve LLMs, they face several main challenges. First, organizations hesitate to share their valuable data with others. Second, competition between organizations creates trust problems during collaboration. Third, new privacy laws require organizations to be able to delete specific data when requested, which is especially difficult when multiple organizations are learning from shared data. Traditional federated learning approaches do not address these interconnected challenges, particularly in scenarios where participants cannot fully trust each other or the central aggregator. To overcome these limitations, we propose a hybrid blockchain-based federated learning framework that uniquely combines public and private blockchain architectures with multi-agent reinforcement learning. Our framework enables transparent sharing of model update through the public blockchain while protecting sensitive computations in private chains. Each organization operates as an intelligent agent, using Q-learning to optimize its participation strategy and resource allocation, thus aligning individual incentives with collective goals. Notably, we introduce an efficient unlearning mechanism based on Low-Rank Adaptation (LoRA) that enables selective removal of specific data contributions without compromising the model's overall performance. Through extensive experimentation on real-world datasets, we demonstrate that our framework effectively balances privacy protection, trust establishment, and regulatory compliance while maintaining high model performance.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2412.13551

PDF

https://arxiv.org/pdf/2412.13551

OA Status

green

Cited By

1

Related Works

10

OpenAlex ID

https://openalex.org/W4405626248

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4405626248

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2412.13551

Digital Object Identifier

Title

Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration

Work title

Type

preprint

OpenAlex work type

Language

en

Primary language

Publication year

2024

Year of publication

Publication date

2024-12-18

Full publication date if available

Authors

Xuhan Zuo, Minghao Wang, Tianqing Zhu, Shui Yu, Wanlei Zhou

List of authors in order

Landing page

https://arxiv.org/abs/2412.13551

Publisher landing page

PDF URL

https://arxiv.org/pdf/2412.13551

Direct link to full text PDF

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2412.13551

Direct OA link when available

Concepts

Blockchain, Knowledge management, Organizational learning, Computer science, Business, Process management, Computer security

Top concepts (fields/topics) attached by OpenAlex

Cited by

1

Total citation count in OpenAlex

Citations by year (recent)

2025: 1

Per-year citation counts (last 5 years)

Related works (count)

10

Other works algorithmically related by OpenAlex

Full payload