LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
This work addresses efficiency and scalability issues in quantum federated learning for researchers and practitioners, though it appears incremental by combining existing LLM and QFL techniques.
The paper tackles the challenge of improving efficiency and performance in quantum federated learning (QFL) by distilling a large language model (LLM) to act as a reinforcement agent that optimizes QFL processes, resulting in reduced communication costs and faster convergence.
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.