When Quantum Federated Learning Meets Blockchain in 6G Networks

TL;DR

The paper proposes QFLchain, a framework that fuses quantum federated learning with blockchain to enable scalable, secure AI in 6G networks. It analyzes four pillars—communication/consensus overhead, scalability/storage, energy efficiency, and security vulnerability—and presents a case study showing potential training-performance gains over state-of-the-art methods in simulations. The work highlights the benefits of quantum communication, quantum consensus, and entanglement-based storage to address the limitations of classical FL-blockchain systems, while also acknowledging that many claims remain theoretical or simulation-based without hardware validation. Overall, QFLchain lays a foundation for decentralized, quantum-resilient AI architectures in future 6G ecosystems, with significant implications for privacy, efficiency, and trust in distributed edge learning.

Abstract

Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the computational advantages of quantum devices, QFL offers significant improvements in learning efficiency and resilience against quantum-era threats. However, future 6G environments are expected to be highly dynamic, decentralized, and data-intensive, which necessitates moving beyond traditional centralized federated learning frameworks. To meet this demand, blockchain technology provides a decentralized, tamper-resistant infrastructure capable of enabling trustless collaboration among distributed quantum edge devices. This paper presents QFLchain, a novel framework that integrates QFL with blockchain to support scalable and secure 6G intelligence. In this work, we investigate four key pillars of \textit{QFLchain} in the 6G context: (i) communication and consensus overhead, (ii) scalability and storage overhead, (iii) energy inefficiency, and (iv) security vulnerability. A case study is also presented, demonstrating potential advantages of QFLchain, based on simulation, over state-of-the-art approaches in terms of training performance.

Figures (4)

• Figure 1: The proposed QFLchain framework. The architecture integrates global, task-specific, and local blockchains to coordinate model publishing, decentralized training, block generation, and ledger propagation across quantum edge devices.
• Figure 2: QFLchain’s blockchain storage structure, where global models and updates are sequentially stored with metadata for auditing, rollback, and recovery.
• Figure 3: Performance comparison between different types of decentralized QFL approaches on MNIST data using 5 clients. The approaches include star, circle, and random.
• Figure 4: Performance comparison between CNN, QNN, FL, QFL, and our proposed Decentralized-QFL on MNIST dataset using 5 clients. We use a circle-based decentralized-QFL approach in this comparison.