Optimizing quantum federated learning: addressing non-I I D data challenges with global data sharing in weighted model averaging and clustering-based parameter selection
Zhiqiang Guo, Jianbin Hu, Wenyu Yang, Yunqian Wang
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 .
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 .
Federated Learning (FL) has become increasingly popular across different sectors, offering a way for clients to work together to train a global model without sharing sensitive data. It involves multiple rounds of communication between the global model and participating clients, which introduces several challenges like high communication costs, heterogeneous client data, prolonged processing times, and increased vulnerability to privacy threats. In recent years, the convergence of federated learning and parameterized quantum circuits has sparked significant research interest, with promising implications for fields such as healthcare and finance. By enabling decentralized training of quantum models, it allows clients or institutions to collaboratively enhance model performance and outcomes while preserving data privacy. Recognizing that Fisher information can quantify the amount of information that a quantum state carries under parameter changes, thereby providing insight into its geometric and statistical properties. We intend to leverage this property to address the aforementioned challenges. In this work, we propose a Quantum Federated Learning (QFL) algorithm that makes use of the Fisher information computed on local client models, with data distributed across heterogeneous partitions. This approach identifies the critical parameters that significantly influence the quantum model's performance, ensuring they are preserved during the aggregation process. Our research assessed the effectiveness and feasibility of QFL by comparing its performance against other variants, and exploring the benefits of incorporating Fisher information in QFL settings. Experimental results on ADNI and MNIST datasets demonstrate the effectiveness of our approach in achieving better performance and robustness against the quantum federated averaging method.
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.
Quantum federated learning (QFL) enables collaborative training of variational quantum circuits across decentralized clients, but client drift under non-IID data distributions degrades standard Federated Averaging (FedAvg). Existing aggregation methods either introduce tunable hyperparameters that depend on unknown data heterogeneity or incur structural costs such as order-dependent error propagation. We propose Kernel-Weighted Aggregation (KWA), a hyperparameter-free aggregation strategy that constructs a non-parametric kernel density estimate over the received client parameter vectors at each communication round. Clients in high-density regions receive greater voting power, and isolated clients are automatically attenuated. The kernel bandwidth is the median of all pairwise squared distances. We evaluate KWA against six state-of-the-art QFL methods and five classical robust baselines on five binary MNIST digit-pair tasks under IID, Dir(0.5), and Dir(0.1) distributions with a unified four-qubit benchmark. Under IID data, KWA recovers the performance of FedAvg. Under Dir(0.5), KWA attains the highest average accuracy (0.7120), followed by FedAvg (0.7093). The margin at N=4 clients is not statistically significant. It grows to +0.023 at N=16 in the client scaling experiment. Under this distribution, the five classical robust baselines also rank below FedAvg. Under Dir(0.1), KWA exceeds FedAvg by 0.028 on average, with a pooled paired t-test p=0.068. Larger-circuit and three-class experiments show that the advantage does not yet transfer consistently beyond the four-qubit binary setting. KWA thus provides a hyperparameter-free aggregation strategy that recovers FedAvg under IID conditions, adapts to client drift, and requires no manually tuned hyperparameters.
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.
The integration of quantum machine learning into federated learning paradigms is poised to transform the future of technologies that depend on diverse machine learning methodologies. This research delves into Quantum Federated Learning (QFL), presenting an initial framework modeled on the Federated Averaging (FedAvg) algorithm, implemented via Qiskit. Despite its potential, QFL encounters critical challenges, including (i) susceptibility to a single point of failure , (ii) communication bottlenecks, and (iii) uncertainty in model convergence. Subsequently, we dive deeper into QFL and propose an innovative alternative to traditional server-based QFL. Our approach introduces a chained continuous QFL framework (ccQFL), which eliminates the need for a central server and the FedAvg method. In our framework, clients engage in a chained continuous training process, where they exchange models and collaboratively enhance each other’s performance. This approach improves both the efficiency of communication and the accuracy of the training process. Our experimental evaluation includes a proof-of-concept to demonstrate initial feasibility and a prototype study simulating TCP/IP communication between clients. This simulation enables concurrent operations, verifying the potential of ccQFL for real-world applications. We examine various datasets, including Iris, MNIST, synthetic and Genomic, covering a range of data sizes from small to large. For further validity of our proposed method, we extend our experimental analysis in other frameworks such as PennyLane and TensorCircuit where we include various ablation studies covering major considerations and factors that impact the framework to study validity, robustness, practicality, and others. Our results show that the ccQFL framework achieves model convergence, and we evaluate other critical metrics such as performance and communication delay. In addition, we provide a theoretical analysis to establish and discuss many factors such as model convergence, communication costs, etc.