Federated Learning over Blockchain-Enabled Cloud Infrastructure

Computer Science > Machine Learning [Submitted on 21 Apr 2026] Federated Learning over Blockchain-Enabled Cloud Infrastructure Saloni Garg, Amit Sagtani, Kamal Kant Hiran 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. Comments: 7 pages, 5 figures, 2 tables Subjects: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC) ACM classes: I.2.6; K.6.5 Cite as: arXiv:2604.20062 [cs.LG]   (or arXiv:2604.20062v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.20062 Focus to learn more Journal reference: in 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG), Indore, India, Dec. 2025, pp. 1-7 Related DOI: https://doi.org/10.1109/ICTBIG68706.2025.11323669 Focus to learn more Submission history From: Saloni Garg [view email] [v1] Tue, 21 Apr 2026 23:51:00 UTC (634 KB) Access Paper: view license Current browse context: cs.LG < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CR cs.DC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)