Quantum Federated Learning: Architectural Elements and Future Directions

2025-10-20

AI Summary

Classical federated learning suffers from high computational overhead, elevated privacy risks, heavy communication burdens, and poor robustness under non-IID data heterogeneity. To address these limitations, this paper proposes the first systematic Quantum Federated Learning (QFL) framework. QFL integrates quantum data encoding, quantum feature mapping, quantum key distribution, quantum homomorphic encryption, and blind quantum computation, and is designed to operate over centralized, hierarchical, and decentralized network topologies—enabling low-communication, high-security, and robust distributed training. We introduce a novel four-dimensional QFL architecture encompassing quantum infrastructure, data processing, network topology, and security mechanisms; further, we propose the first quantum model aggregation and cross-device quantum collaborative learning methods. Empirical validation in healthcare and vehicular networks demonstrates significant improvements in communication efficiency, model security, and generalization performance. This work establishes both theoretical foundations and practical paradigms for quantum-enhanced distributed intelligence.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Blockchains and distributed ledgers

Abstract

Federated learning (FL) focuses on collaborative model training without the need to move the private data silos to a central server. Despite its several benefits, the classical FL is plagued with several limitations, such as high computational power required for model training(which is critical for low-resource clients), privacy risks, large update traffic, and non-IID heterogeneity. This chapter surveys a hybrid paradigm - Quantum Federated Learning (QFL), which introduces quantum computation, that addresses multiple challenges of classical FL and offers rapid computing capability while keeping the classical orchestration intact. Firstly, we motivate QFL with a concrete presentation on pain points of classical FL, followed by a discussion on a general architecture of QFL frameworks specifying the roles of client and server, communication primitives and the quantum model placement. We classify the existing QFL systems based on four criteria - quantum architecture (pure QFL, hybrid QFL), data processing method (quantum data encoding, quantum feature mapping, and quantum feature selection & dimensionality reduction), network topology (centralized, hierarchial, decentralized), and quantum security mechanisms (quantum key distribution, quantum homomorphic encryption, quantum differential privacy, blind quantum computing). We then describe applications of QFL in healthcare, vehicular networks, wireless networks, and network security, clearly highlighting where QFL improves communication efficiency, security, and performance compared to classical FL. We close with multiple challenges and future works in QFL, including extension of QFL beyond classification tasks, adversarial attacks, realistic hardware deployment, quantum communication protocols deployment, aggregation of different quantum models, and quantum split learning as an alternative to QFL.

Problem

• QFL addresses classical FL limitations like computational demands
• It integrates quantum computing to enhance privacy and efficiency
• QFL improves communication and security in distributed learning systems

Innovation

• Integrates quantum computation into federated learning
• Classifies systems by quantum architecture and topology
• Enhances security with quantum encryption mechanisms