Federated Learning over Blockchain-Enabled Cloud Infrastructure

2026-04-21

AI Summary

This study addresses the challenges of data privacy leakage, security risks, and regulatory compliance inherent in traditional centralized machine learning within cloud-edge environments. To overcome these issues, the authors propose a novel cloud-edge collaborative architecture that integrates federated learning with blockchain technology. They introduce the first four-dimensional taxonomy—encompassing coordination mechanisms, consensus algorithms, data storage, and trust models—to systematically evaluate existing blockchain-enabled federated learning (BCFL) frameworks. The work provides an in-depth comparative analysis of two representative approaches, MORFLB and FBCI-SHS, elucidating their respective strengths and limitations. Building on this assessment, the paper identifies key open challenges and outlines a forward-looking research agenda centered on adaptability, resilience, and standardization.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Federated Learning
• Blockchain
• Privacy
• Data Breach
• Regulatory Compliance

Innovation

• Federated Learning
• Blockchain
• Cloud-Edge Computing
• Architectural Categorization
• Trust Model