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This paper surveys the integration of Federated Learning (FL) and blockchain technology within cloud-edge infrastructures to address data privacy and security concerns in machine learning. It proposes a four-dimensional architectural categorization based on coordination, consensus, storage, and trust. The paper then compares two frameworks, MORFLB and FBCI-SHS, and concludes by outlining challenges and future research directions for Blockchain-enabled Federated Learning (BCFL) systems.
Blockchain-integrated Federated Learning offers a promising path to privacy-preserving machine learning, but faces significant challenges in standardization and adaptability across diverse applications.
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.