Federated Learning over Blockchain-Enabled Cloud Infrastructure

TL;DR

This paper explores integrating federated learning with blockchain technology in cloud-edge environments to enhance privacy, security, and compliance, presenting architectural frameworks, comparative analyses of two frameworks, and future research directions.

Contribution

It introduces a detailed four-dimensional architecture for BCFL systems and compares two innovative frameworks for transportation and healthcare applications.

Findings

Proposes a comprehensive architectural categorization for BCFL systems.

Analyzes two frameworks: MORFLB and FBCI-SHS, highlighting their strengths and limitations.

Identifies key challenges and suggests future research directions for resilient BCFL systems.

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.