양자 연합 학습 이질성 데이터와 시스템 차이점이 수렴에 미치는 영향

📝 Abstract

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.

💡 Analysis

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.

📄 Content

1 Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions Ratun Rahman, Dinh C. Nguyen, Christo Kurisummoottil Thomas, and Walid Saad, Fellow, IEEE Abstract—Quantum federated learning (QFL) combines quan- tum 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 clients, and they do not account for real-world variances in quantum data distributions, encoding techniques, hardware noise levels, and computational capacity. These dif- ferences 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. Fi- nally, we discuss potential future research areas for constructing robust and scalable heterogeneous QFL frameworks. Index Terms—Quantum networks, quantum learning, quan- tum federated learning. I. INTRODUCTION Q UANTUM machine learning (QML) is a promising ap- proach in machine learning (ML) as it can process com- plex and large-scale data at an unprecedented speed by lever- aging quantum phenomena such as superposition, entangle- ment, quantum parallelism, and quantum interference. QML integrates quantum physics with advanced computational tech- niques in ML across distributed quantum devices known as noisy intermediate-scale quantum (NISQ) devices [1]. How- ever, in most conventional QML frameworks, the data is collected and processed on a central server, raising significant privacy concerns and exposing the data to various data-based attacks, even with quantum encoding [2]. Furthermore, the large-scale nature of high-dimensional data transfer creates a high communications overhead, resulting in slower overall performance and scalability challenges [3]. These limitations make QML unsuitable for practical deployment in scenarios that involve continuous data processing and sensitive data handling. A promising approach to address the aforementioned chal- lenge is through the use of quantum federated learning Ratun Rahman and Dinh C Nguyen are with the Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA, emails: rr0110@uah.edu, dinh.nguyen@uah.edu Christo Kurisummoottil Thomas is with the Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, and Walid Saad is with the Bradley Department of Electrical and Computer Engineering, Virginia Tech, Alexandria, VA, 22305, USA, emails: christokt@vt.edu, walids@vt.edu. (QFL) [4]. QFL combines quantum computing with federated learning (FL) to perform ML tasks across distributed networks. In a QFL configuration, multiple clients equipped with quan- tum devices conduct local data encoding and use quantum states and unique quantum features such as superposition and entanglement [4], [5]. Each client creates a local model by processing data in quantum form, which enables the use of quantum computational benefits such as faster processing rates and more effective handling of complex data sets. These local models are modified by sending the improved parameters back to a central server, aggregating the modifications. The server uses complex quantum algorithms to conduct this aggregation, which are particularly intended to improve the collective learning process throughout the quantum network. This type of model aggregation not only protects data privacy, but also uses quantum computing capabilities to improve learning outcomes, particularly when dealing with complex large-scale computational problems [5], [6]. However, despite such promising results, existing QFL frameworks [2], [5]–[7] mainly assume homogeneous clients, ignoring the inherent heterogeneity that exists in real-world quantum systems. In practice, QFL clients have substantially distinct quantum data distributions, encoding strategies [8], hardware noise levels, and computational abilities, resulting in differences in local model training and global aggregation. This variability causes model divergence, slow convergence, and suboptimal learning performance, making generalization difficult across different quantum devices. Variations in the depth of quantum circuits, decoherence rates, and quantum gate fidelities also exacerbate learning differences between clients. As a result, tackling heterogeneous QFL is critical for achieving robust, scalable, and practical federated learning [9]. Th

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