LLM-QFL: Distilling Large Language Model for Quantum Federated Learning

• URL: http://arxiv.org/abs/2505.18656v1
• Date: Sat, 24 May 2025 11:49:21 GMT
• Title: LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
• Authors: Dev Gurung, Shiva Raj Pokhrel,
• Abstract summary: We adapt large language models (LLMs) to quantum federated learning (QFL) to boost efficiency and performance.<n>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.<n> Experiments show significant efficiency gains.
• Score: 13.782852293291493
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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