将联邦学习与区块链结合，提升物联网云端数据隐私与安全。
Federated Learning over Blockchain-Enabled Cloud Infrastructure
- 构建四维架构分类体系，涵盖协调、共识、存储与信任机制。
- 对比分析MORFLB与FBCI-SHS框架，验证其在交通与医疗场景的有效性。
- 提出未来研究方向，推动可适应、抗攻击的标准化系统发展。
物联网设备的兴起与云计算的普及催生了数据驱动智能的新时代。传统集中式机器学习需将大量数据集中存储，易引发数据泄露、隐私侵犯及合规风险。本文深入探讨联邦学习（FL）与区块链技术在云边协同环境中的融合，提出一个包含协调框架、共识算法、数据存储与信任模型的四维架构分类体系，有效应对上述挑战。文中详细对比两种前沿框架：面向智能交通系统的多目标强化联邦学习区块链（MORFLB），以及面向可持续医疗系统的联邦区块链-物联网框架（FBCI-SHS），剖析其创新贡献与固有局限。通过系统性文献评述，明确本研究在现有知识体系中的独特性。最后，总结核心挑战并提出前瞻性研究路径，强调在多样化应用场景中推进自适应、鲁棒且标准化的区块链联邦学习（BCFL）系统建设。
原文摘要 · Abstract (English)
The rise of IoT devices and the uptake of cloud computing have informed a new era of data-driven intelligence. Traditional centralized machine learning models that require a large volume of data to be stored in a single location have therefore become more susceptible to data breaches, privacy violations, and regulatory non-compliance. This report presents a thorough examination of the merging of Federated Learning (FL) and blockchain technology in a cloud-edge setting, demonstrating it as an effective solution to the stated concerns. We are proposing a detailed four-dimensional architectural categorization that meticulously assesses coordination frameworks, consensus algorithms, data storage practices, and trust models that are significant to these integrated systems. The manuscript presents a comprehensive comparative examination of two cutting-edge frameworks: the Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB), which is designed for intelligent transportation systems, and the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS), elucidating their distinctive contributions and inherent limitations. Lastly, we engage in a thorough evaluation of the literature that integrates a comparative perspective on current frameworks to discern the singular nature of this research within existing knowledge systems. The manuscript culminates in delineating the principal challenges and offering a strategic framework for prospective research trajectories, emphasizing the advancement of adaptive, resilient, and standardized BCFL systems across diverse application domains.
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