Scalable Data Engineering Architectures for Federated Learning in Decentralized Cloud Environments

  • Authors

    • Laura Conti Business Analyst, Deloitte Italy, Italy. Author
    • Dr. Andrew Collins Professor, University of Oxford, UK. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2021PII4V3D

    Published 10-05-2021

  • Federated Learning, Decentralized Cloud, Scalable Data Engineering, Edge Computing, Data Pipeline Orchestration, Privacy-preserving Machine Learning, Kubernetes, Data Heterogeneity, Distributed Systems, Secure Data Sharing

    Issue

    Vol. 4 No. 2 (2021)

    Section

    Articles

    How to Cite

    [1]
    L. Conti and A. Collins, “Scalable Data Engineering Architectures for Federated Learning in Decentralized Cloud Environments”, IJDEIC, vol. 4, no. 2, pp. 01–12, Oct. 2021, doi: 10.67228/30715717/IJDEIC-2021PII4V3D.

  • Abstract

    The growing adoption of Federated Learning (FL) is reshaping the way machine learning models are trained across distributed, privacy-sensitive datasets. However, the scalable and efficient orchestration of data engineering pipelines in decentralized cloud environments remains a significant challenge. This paper presents a comprehensive architectural framework for scalable data engineering tailored for FL in heterogeneous and resource-constrained environments. By integrating modern distributed computing paradigms, such as Kubernetes-based orchestration, edge-aware data preprocessing, and secure federated communication, we propose a modular architecture that addresses data heterogeneity, scalability, and compliance. A case study in a healthcare IoT scenario validates the performance and flexibility of the proposed system. Our work serves as a blueprint for deploying robust FL systems in real-world decentralized cloud ecosystems.

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