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IEEE Transactions on Artificial Intelligence

Performance Analysis and Design of a Weighted Personalized Quantum Federated Learning

Dev Gurung, Shiva Raj Pokhrel

Published Feb 25, 2025• 15 citations
Publisher

Abstract

Advances in federated and quantum computing have improved data privacy and efficiency in distributed systems. Quantum federated learning (QFL), like its classical counterpart, classic federated learning (CFL), struggles with challenges in heterogeneous environments. To address these, we proposewp-QFL, a weighted personalized approach with quantum federated averaging (qFedAvg), tackling non-IID data and local model drift. While CFL personalization has been well explored, its application to QFL remains underdeveloped due to inherent differences. The proposedwp-QFLfills this gap by adapting to data heterogeneity with weighted personalization and drift correction. The code implementation is available athttps://github.com/s222416822/wpQFL.

DOI
10.1109/tai.2025.3545393

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Advances in federated and quantum computing have improved data privacy and efficiency in distributed systems. Quantum federated learning (QFL), like its classical counterpart, classic federated learning (CFL), struggles with challenges in heterogeneous environments. To address these, we proposewp-QFL, a weighted personalized approach with quantum federated averaging (qFedAvg), tackling non-IID data and local model drift. While CFL personalization has been well explored, its application to QFL remains underdeveloped due to inherent differences. The proposedwp-QFLfills this gap by adapting to data heterogeneity with weighted personalization and drift correction. The code implementation is available athttps://github.com/s222416822/wpQFL.
Abstract This paper addresses Quantum Federated Learning (QFL), an adaptation of classical federated learning for quantum computing that enables collaboration among multiple quantum devices. A significant challenge in QFL arises from non-independent and identically distributed (non-IID) data, primarily due to inherent variations in quantum devices and environments, which substantially degrades performance. To mitigate this, we propose novel QFL algorithms that employ weighted averaging and clustering-optimized averaging strategies. Specifically, Quantum Weighted Federated Averaging (QW-FedAvg) is introduced, which utilizes a model aggregation strategy combining quantum weighted averaging with a global data-sharing mechanism to reduce the impact of non-IID data. Building on this, we further propose the Quantum Federated Clustering-Optimized Averaging (QF-COA) algorithm. QF-COA enhances performance by selecting client-side training parameters that demonstrate superior efficacy through a clustering method, thereby further minimizing the adverse effects of non-IID data on model performance. Our models were trained and evaluated using the MNIST and Fashion-MNIST datasets. Simulation results demonstrate that the proposed algorithms improve accuracy by 2% to 5% in non-IID environments compared to traditional quantum federated averaging algorithms. Additionally, these advancements lead to enhanced communication efficiency and increased robustness. These improvements contribute to the greater generalization and adaptability of QFL frameworks, offering both theoretical insights and practical guidance for future research in the field. The source code is available at https://github.com/wang-lab-repository/qavg-gzq.git .
Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve optimal model training performance primarily due to inherent heterogeneity in terms of (i) quantum noise where current quantum devices are subject to varying levels of noise due to varying device quality and susceptibility to quantum decoherence, and (ii) heterogeneous data distributions where data across participating quantum devices are naturally non-independent and identically distributed (non-IID). To address these challenges, we propose a novel integrated sporadic-personalized approach called SPQFL that simultaneously handles quantum noise and data heterogeneity in a single QFL framework. It is featured in two key aspects: (i) for quantum noise heterogeneity, we introduce a notion of sporadic learning to tackle quantum noise heterogeneity across quantum devices, and (ii) for quantum data heterogeneity, we implement personalized learning through model regularization to mitigate overfitting during local training on non-IID quantum data distributions, thereby enhancing the convergence of the global model. Moreover, we conduct a rigorous convergence analysis for the proposed SPQFL framework, with both sporadic and personalized learning considerations. Theoretical findings reveal that the upper bound of the SPQFL algorithm is strongly influenced by both the number of quantum devices and the number of quantum noise measurements. Extensive simulation results in real-world datasets also illustrate that the proposed SPQFL approach yields significant improvements in terms of training performance and convergence stability compared to the state-of-the-art methods.
Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quantum devices while ensuring data privacy in quantum networks. Despite recent research efforts, existing QFL frameworks struggle to achieve optimal model training performance primarily due to inherent heterogeneity in terms of (i) quantum noise where current quantum devices are subject to varying levels of noise due to varying device quality and susceptibility to quantum decoherence, and (ii) heterogeneous data distributions where data across participating quantum devices are naturally non-independent and identically distributed (non-IID). To address these challenges, we propose a novel integrated sporadic-personalized approach called SPQFL that simultaneously handles quantum noise and data heterogeneity in a single QFL framework. It is featured in two key aspects: (i) for quantum noise heterogeneity, we introduce a notion of sporadic learning to tackle quantum noise heterogeneity across quantum devices, and (ii) for quantum data heterogeneity, we implement personalized learning through model regularization to mitigate overfitting during local training on non-IID quantum data distributions, thereby enhancing the convergence of the global model. Moreover, we conduct a rigorous convergence analysis for the proposed SPQFL framework, with both sporadic and personalized learning considerations. Theoretical findings reveal that the upper bound of the SPQFL algorithm is strongly influenced by both the number of quantum devices and the number of quantum noise measurements. Extensive simulation results in real-world datasets also illustrate that the proposed SPQFL approach yields significant improvements in terms of training performance and convergence stability compared to the state-of-the-art methods.
Advances in federated and quantum machine learning have improved data privacy and computational efficiency in distributed systems. Quantum Federated Learning is a novel framework which combines the superior computational power of quantum computer and scalability and data privacy concern in classical federated learning framework. However, similarly to classical counterpart, quantum distributed training version still faces with challenges in heterogeneous data settings, leading to downgrade performance. To address this issue, a personalized quantum federated learning is introduced, where the local models are divided into two parts. While the major part will be aggregated normally with vanilla federated learning, the minor layers are keep locally, without transmitting. The proposed model has shown its supremacy capabilities in difficult scenarios in multiple datasets, which is shown comprehensively through simulations and numerical results.
Quantum federated learning (QFL) combines quantum computing and federated learning to enable decentralized model training while maintaining data privacy. QFL can improve computational efficiency and scalability by taking advantage of quantum properties such as superposition and entanglement. However, existing QFL frameworks largely focus on homogeneity among quantum \textcolor{black}{clients, and they do not account} for real-world variances in quantum data distributions, encoding techniques, hardware noise levels, and computational capacity. These differences can create instability during training, slow convergence, and reduce overall model performance. In this paper, we conduct an in-depth examination of heterogeneity in QFL, classifying it into two categories: data or system heterogeneity. Then we investigate the influence of heterogeneity on training convergence and model aggregation. We critically evaluate existing mitigation solutions, highlight their limitations, and give a case study that demonstrates the viability of tackling quantum heterogeneity. Finally, we discuss potential future research areas for constructing robust and scalable heterogeneous QFL frameworks.