Federated Learning over Blockchain-Enabled Cloud Infrastructure

2026-04-23 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Blockchain & Distributed Ledger Technology, Cloud Computing & IT Infrastructure · Depth: Expert, quick

Summary

This report, submitted on April 21, 2026, examines the integration of Federated Learning (FL) and blockchain technology within cloud-edge environments to address data privacy, security, and regulatory compliance concerns arising from the proliferation of IoT devices and cloud computing. It proposes a four-dimensional architectural categorization that evaluates coordination frameworks, consensus algorithms, data storage practices, and trust models crucial for these integrated systems. The paper provides a comparative analysis of two frameworks: the Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for intelligent transportation systems and the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS). It concludes by outlining key challenges and suggesting future research directions for developing adaptive, resilient, and standardized Blockchain-Federated Learning (BCFL) systems across various application domains.

Key takeaway

For CTOs and VPs of Engineering evaluating secure machine learning deployments, this analysis highlights how combining Federated Learning with blockchain can mitigate privacy and compliance risks inherent in centralized data models. You should consider adopting a four-dimensional architectural assessment framework to design and implement robust, distributed ML systems, particularly for sensitive applications like healthcare or transportation, ensuring data integrity and regulatory adherence.

Key insights

Integrating Federated Learning with blockchain in cloud-edge environments enhances data privacy and security.

Principles

• Centralized ML models risk data breaches.
• BCFL systems require robust coordination and trust.
• Adaptive BCFL systems are crucial for diverse applications.

Method

The authors propose a four-dimensional architectural categorization to assess coordination frameworks, consensus algorithms, data storage, and trust models in integrated FL-blockchain systems.

In practice

• Apply MORFLB for intelligent transportation.
• Utilize FBCI-SHS for sustainable healthcare.
• Evaluate trust models in distributed ML.

Topics

• Federated Learning
• Blockchain Technology
• Cloud-Edge Computing
• Data Privacy
• Architectural Categorization

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.