{"ID":2831409,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09958","arxiv_id":"2512.09958","title":"When Quantum Federated Learning Meets Blockchain in 6G Networks","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.","short_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 threat...","url_abs":"https://arxiv.org/abs/2512.09958","url_pdf":"https://arxiv.org/pdf/2512.09958v1","authors":"[\"Dinh C. Nguyen\",\"Md Bokhtiar Al Zami\",\"Ratun Rahman\",\"Shaba Shaon\",\"Tuy Tan Nguyen\",\"Fatemeh Afghah\"]","published":"2025-12-09T21:44:18Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.DC\"]","methods":"[]","has_code":false}