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

**Authors:** Xuhan Zuo, Minghao Wang, Tianqing Zhu, Shui Yu, Wanlei Zhou  
**arXiv:** [2412.13551](https://arxiv.org/abs/2412.13551)  
**Date:** 2024-12-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2412.13551v1/

## Key points

- Authors address challenges of utilizing large language models (LLMs) across different organizations
- Proposed hybrid blockchain-based federated learning framework integrates public and private blockchain architectures with multi-agent reinforcement learning
- Framework facilitates transparent sharing of model updates through public blockchain while safeguarding sensitive computations within private chains
- Each organization functions as an intelligent agent leveraging Q-learning to optimize participation strategy and resource allocation
- Framework aligns individual incentives with collective goals to promote collaboration and mitigate trust issues among organizations
- Issues such as data sharing reluctance, trust problems, and compliance with privacy laws present obstacles to collaborative efforts in improving LLMs
- Introduction of efficient unlearning mechanism based on Low-Rank Adaptation (LoRA) enables selective removal of specific data contributions without compromising overall model performance
- Extensive experimentation on real-world datasets demonstrates effectiveness in balancing privacy protection, trust establishment, regulatory compliance, and high model performance

## Summary

In their paper titled "Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration," authors Xuhan Zuo, Minghao Wang, Tianqing Zhu, Shui Yu, and Wanlei Zhou address the challenges associated with effectively utilizing large language models (LLMs) across different organizations. The authors propose a novel hybrid blockchain-based federated learning framework that integrates public and private blockchain architectures with multi-agent reinforcement learning to overcome these challenges. This innovative framework facilitates transparent sharing of model updates through the public blockchain while safeguarding sensitive computations within private chains. Each organization functions as an intelligent agent leveraging Q-learning to optimize its participation strategy and resource allocation in this framework. By aligning individual incentives with collective goals, the framework promotes collaboration while mitigating trust issues among organizations. The use of LLMs has revolutionized the processing of human language by computers. However, issues such as data sharing reluctance, trust problems stemming from competition between organizations, and compliance with new privacy laws present significant obstacles to collaborative efforts in improving LLMs. Traditional federated learning approaches fall short in addressing these interconnected challenges, particularly in scenarios where participants lack full trust in each other or a central aggregator. To address these limitations and promote effective cross-organizational collaboration in leveraging LLMs for language processing applications, the authors introduce an efficient unlearning mechanism based on Low-Rank Adaptation (LoRA). This mechanism enables selective removal of specific data contributions without compromising the overall performance of the model. Through extensive experimentation on real-world datasets, the authors demonstrate that their proposed framework effectively balances privacy protection, trust establishment, and regulatory compliance while maintaining high model performance. By combining cutting-edge technologies such as blockchain architecture and reinforcement learning with innovative mechanisms like LoRA for unlearning specific data contributions, this research offers a promising solution for enhancing cross-organizational collaboration in leveraging LLMs for language processing applications.