Federated Learning over Blockchain-Enabled Cloud Infrastructure

• URL: http://arxiv.org/abs/2604.20062v1
• Date: Tue, 21 Apr 2026 23:51:00 GMT
• Title: Federated Learning over Blockchain-Enabled Cloud Infrastructure
• Abstract summary: The rise of IoT devices and the uptake of cloud computing have informed a new era of data-driven intelligence.<n>Traditional centralized machine learning models that require a large volume of data to be stored in a single location have become more susceptible to data breaches, privacy violations, and regulatory non-compliance.<n>This report presents a thorough examination of the merging of Federated Learning (FL) and blockchain technology in a cloud-edge setting.
• Score: 0.0695155813793092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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