LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 24 May 2025.
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33 of 54 references.
References · 33
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- 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptationscited by 144
21 more
- 2024Barren Plateaus in Variational Quantum Computingcited by 130
- 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 124
- 2021Federated Quantum Machine Learningcited by 82
- 2024NetLLM: Adapting Large Language Models for Networkingcited by 76
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- 2024FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Modelcited by 73
- 2022Recovering Private Text in Federated Learning of Language Modelscited by 67
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- 2024PrE-Text: Training Language Models on Private Federated Data in the Age of LLMscited by 29
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- 2022Slimmable Quantum Federated Learningcited by 23
- 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
- 2022Federated Quantum Natural Gradient Descent for Quantum Federated Learningcited by 14
- 2024Quantum Federated Learning Experiments in the Cloud with Data Encodingcited by 7
- 2023A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Designcited by 5
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