Federated Learning over Blockchain-Enabled Cloud Infrastructure

arXiv cs.LG / 4/23/2026

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Key Points

The paper argues that combining Federated Learning (FL) with blockchain in cloud-edge environments can mitigate risks from centralized ML, including data breaches, privacy violations, and regulatory non-compliance.
It proposes a four-dimensional architecture taxonomy covering coordination mechanisms, consensus algorithms, data storage approaches, and trust models for blockchain-enabled FL systems.
The study compares two contemporary frameworks—MORFLB (for intelligent transportation systems) and FBCI-SHS (for sustainable healthcare)—highlighting both their contributions and limitations.
It concludes with an evaluation of related literature and outlines key open challenges, proposing a research roadmap toward more adaptive, resilient, and standardized BCFL systems across multiple application domains.

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.