用大模型提升量子联邦学习效率，边学边优化通信与选人。
LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
- 在量子联邦学习中蒸馏大模型，本地微调并保护隐私。
- 通信轮数减少，收敛速度更快，支持资源受限设备部署。
- 适合关注高效联邦学习与量子计算融合的研究者。
受大型语言模型（LLM）能力的启发，本研究将它们引入量子联邦学习（QFL），以提升效率与性能。我们提出一种联邦微调方法，在QFL中蒸馏一个LLM，使每个客户端能在本地适应自身数据的同时，保持隐私并减少不必要的全局更新。该微调后的LLM还充当强化代理，通过调整优化器步长、减少通信轮次以及智能选择客户端来优化QFL。实验表明显著提升了效率。本工作开创了LLM与QFL的协同机制：(i) 实用性：降低通信开销，加快收敛；(ii) 理论严谨性：提供自适应联邦优化的可证明保证；(iii) 可扩展性：采用PEFT方法（如LoRA、QLoRA）实现在资源受限量子设备上的部署。代码已公开。
原文摘要 · Abstract (English)
Inspired by the power of large language models (LLMs), our research adapts them to quantum federated learning (QFL) to boost efficiency and performance. We propose a federated fine-tuning method that distills an LLM within QFL, allowing each client to locally adapt the model to its own data while preserving privacy and reducing unnecessary global updates. The fine-tuned LLM also acts as a reinforcement agent, optimizing QFL by adjusting optimizer steps, cutting down communication rounds, and intelligently selecting clients. Experiments show significant efficiency gains. We pioneer a synergy between LLM and QFL, offering: i) practical efficiency: Reduced communication costs and faster convergence. ii) theoretical rigor: Provable guarantees for adaptive federated optimization. iii) scalability: PEFT methods (LoRA, QLoRA) enable deployment on resource-constrained quantum devices. Code implementation is available here 1.
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