Quantum Federated Learning: A Comprehensive Survey

2025-08-21

AI Summary

Quantum Federated Learning (QFL) addresses critical challenges in distributed quantum systems—namely, privacy leakage, excessive communication overhead, and suboptimal training efficiency. This work systematically establishes a QFL technical framework, proposing a novel architecture that integrates parameterized quantum circuits, hierarchical federated aggregation, and a lightweight quantum-secure protocol. It further designs an adaptive communication compression mechanism and gradient sparsification strategy tailored for heterogeneous quantum devices. Prototype evaluations are conducted across intelligent transportation, telemedicine, and low-earth-orbit satellite networks. The study introduces the first taxonomy of QFL paradigms, identifies fundamental bottlenecks—including quantum-classical co-modeling and cross-platform secure quantum state transmission—and releases the first lightweight open-source QFL simulation platform. These contributions provide a reproducible methodology and practical paradigm for privacy-enhancing quantum AI.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Security and privacy of machine learning and AI applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field.

Problem

• Combining quantum computing with federated learning for privacy
• Addressing efficient secure training in distributed quantum systems
• Exploring QFL concepts applications and emerging challenges

Innovation

• Combines quantum computing with federated learning
• Integrates privacy-preserving decentralized quantum-enhanced training
• Explores federation architecture and security mechanisms