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.
Quantum Federated Learning: A Comprehensive Survey
Department of Electrical and Computer Engineering, The University of Alabama in Huntsville, USA（电气与计算机工程系，美国阿拉巴马大学亨茨维尔分校）；Faculty of Engineering and Applied Science, Memorial University, Canada（工程与应用科学学院，加拿大纪念大学）；College of Computer Science and Engineering, Nanyang Technological University, Singapore（计算机科学与工程学院，新加坡南洋理工大学）
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the existing static QFL methods, we propose slimmable QFL (SlimQFL) in this article, which is a dynamic QFL framework that can cope with time-varying communication channels and computing energy limitations. This is made viable by leveraging the unique nature of a QNN where its angle parameters and pole parameters can be separately trained and dynamically exploited. Simulation results corroborate that SlimQFL achieves highe…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；National University of Singapore（新加坡国立大学）
Korea University（高丽大学）；Ajou University（亚洲大学）；Deakin University（迪肯大学）；University of Oulu（奥卢大学）
While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that…
From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks
Hung Yen University of Technology and Education（兴安技术与教育大学）；University of Tasmania（塔斯马尼亚大学）；University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Lac Hong University（乐鸿大学）；Manchester Metropolitan University（曼彻斯特城市大学）；International Hellenic University（国际希腊大学）
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. Recently, some purely quantum machine learning models were proposed such as the quantum convolutional neural networks (QCNN) to perform classification on quantum data. However, all of the existing QML models rely on centralized solutions that cannot scale well for large-scale and distributed quantum networks. Hence, it is apropos to consider more practical quantum federated learning (QFL) solutions tailored towards emerging quan…
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
Department of Computer & Information Science & Engineering（计算机与信息科学与工程系）；University of Florida（佛罗里达大学）；Frost Institute for Data Science and Computing（数据科学与计算弗罗斯特研究所）；University of Miami（迈阿密大学）
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Department of Computer Science and Engineering, BITS Pilani Dubai Campus（计算机科学与工程系，比斯帕尼迪拜校区）；Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科技大学）
Distilling A Universal Expert from Clustered Federated Learning
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education（教育部符号计算与知识工程重点实验室）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney（悉尼大学技术学院澳大利亚人工智能研究所）；College of Computer Science, Inner Mongolia University（内蒙古大学计算机科学学院）
Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learning
Research Scholar, Dept. of CSE（研究学者，计算机科学与工程系）；Dept. of CSE, School of Engineering（计算机科学与工程系，工程学院）；PES University, RR Nagar, Bangalore, India（PES大学）；Dayananda Sagar University, Bangalore, India（Dayananda Sagar大学）
FedQNN: Federated Learning using Quantum Neural Networks
Hassan II University of Casablanca（卡萨布兰卡哈桑二世大学）；Quantum United Arab Emirates（阿联酋量子公司）；New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校）；NYUAD Research Institute（纽约大学阿布扎比分校研究院）
The heart of Quantum Federated Learning (QFL) is associated with a distributed learning architecture across several local quantum devices and a more efficient training algorithm for the QFL is expected to minimize the communication overhead among different quantum participants. In this work, we put forth an efficient learning algorithm, namely federated quantum natural gradient descent (FQNGD), applied in a QFL framework which consists of the variational quantum circuit (VQC)-based quantum neural networks (QNN). The FQNGD algorithm admits much fewer training iterations for the QFL model to get…
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning
College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；Modeling and Emulation in E-Government National Engineering Laboratory, Harbin Engineering University（哈尔滨工程大学电子政务建模与仿真国家工程实验室）；School of Mathematics, Physics and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）
Fudan University（复旦大学）；Georgia Institute of Technology（佐治亚理工学院）；Northwestern Polytechnical University（西北工业大学）；Universidad San Pablo-CEU, CEU Universities（CEU大学圣巴勃罗-CEU大学）
Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is always expected to minimize the communication overhead among different quantum participants. In this work, we propose an efficient optimization algorithm, namely federated quantum natural gradient descent (FQNGD), and further, apply it to a QFL framework that is composed of a variational quantum circuit (VQC)-based quantum neural networks (QNN). Compared with stochastic gradient descent methods like Adam and Adagrad, the…
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
University of Science and Technology of China（中国科学技术大学）；Hong Kong Baptist University（香港浸会大学）；University of Maryland College Park（马里兰大学学院公园分校）；The University of Sydney（悉尼大学）
Federated Distillation (FD) is a popular novel algorithmic paradigm for Federated Learning, which achieves training performance competitive to prior parameter averaging based methods, while additionally allowing the clients to train different model architectures, by distilling the client predictions on an unlabeled auxiliary set of data into a student model. In this work we propose FedAUX, an extension to FD, which, under the same set of assumptions, drastically improves performance by deriving maximum utility from the unlabeled auxiliary data. FedAUX modifies the FD training procedure in two …
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences and the University of Chinese Academy of Sciences（北京移动计算与 pervasive 设备重点实验室，计算技术研究所，中国科学院和中国科学院大学）；Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences（北京移动计算与 pervasive 设备重点实验室，计算技术研究所，中国科学院）；Shandong Academy of Intelligent Computing Technology（山东省智能计算技术研究院）；Frontier Sciences and Education, Chinese Academy of Sciences（前沿科学与教育，中国科学院）
