We develop a novel method by combining weighted personalization with quantum federated averaging to address impending challenges such as non-IID data distribution and client drift. The proposed weighted personalized Quantum Federated Learning (wpQFL) dynamically adapts to data heterogeneity, improving performance, validated through theoretical insights and empirical observations.
We develop a novel method by combining weighted personalization with quantum federated averaging to address impending challenges such as non-IID data distribution and client drift. The proposed weighted personalized Quantum Federated Learning (wpQFL) dynamically adapts to data heterogeneity, improving performance, validated through theoretical insights and empirical observations.
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.
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.
Federated learning (FL) has emerged as a compelling paradigm for big-data analytics, enabling collaborative training of a global model across distributed mobile or edge devices without centralizing raw data. Yet conventional FL is challenged by model drift under statistical heterogeneity and by the limited computational budgets of client devices, which together hinder convergence and scalability-particularly when the i.i.d. assumption is violated. To address these limitations, we propose a personalized quantum federated learning (QFL) framework that combines quantum machine learning with distributed optimization to deliver client-specific adaptation atop a compact shared representation. Empirically, our method outperforms state-of-the-art baselines on non-IID workloads while reducing computational complexity through a smaller set of trainable parameters.
Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum federated learning (QFL) overcomes these concerns by distributing model training among several quantum clients, consequently eliminating the requirement for centralized quantum storage and processing. However, in real-life quantum networks, clients frequently differ in terms of hardware capabilities, circuit designs, noise levels, and how classical data is encoded or preprocessed into quantum states. These differences create inherent heterogeneity across clients - not just in their data distributions, but also in their quantum processing behaviors. As a result, training a single global model becomes ineffective, especially when clients handle imbalanced or non-identically distributed (non-IID) data. To address this, we propose a new framework called personalized quantum federated learning (PQFL) for anomaly detection. PQFL enhances local model training at quantum clients using parameterized quantum circuits and classical optimizers, while introducing a quantum-centric personalization strategy that adapts each client's model to its own hardware characteristics and data representation. Extensive experiments show that PQFL significantly improves anomaly detection accuracy under diverse and realistic conditions. Compared to state-of-the-art methods, PQFL reduces false errors by up to 23%, and achieves gains of 24.2% in AUROC and 20.5% in AUPR, highlighting its effectiveness and scalability in practical quantum federated settings.
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.