Federated Learning Frameworks for Privacy-Preserving Smart Applications

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PI6F6C

    Published 03-05-2024

  • Federated Learning, Privacy Preservation, Smart Applications, Edge Computing, Artificial Intelligence, Internet Of Things, Distributed Machine Learning, Secure Aggregation, Differential Privacy, Blockchain, Explainable AI, Secure Edge Intelligence

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    N. Seshagiri, “Federated Learning Frameworks for Privacy-Preserving Smart Applications”, ijmiet, vol. 7, no. 1, pp. 01–19, Mar. 2024, doi: 10.67228/30715628/IJMIET-2024PI6F6C.

  • Abstract

    Internet of Things (IoT), edge computing, and cloud computing has transformed data-driven services across healthcare, transportation, manufacturing, finance, agriculture, and smart cities. These applications generate large volumes of sensitive distributed data, making traditional centralized machine learning unsuitable due to privacy, security, communication, and regulatory challenges. Federated Learning (FL) addresses these issues by enabling collaborative model training without transferring raw data, thereby preserving user privacy.This paper presents a privacy-preserving federated learning framework that integrates secure aggregation, differential privacy, encryption, adaptive communication, and federated optimization for large-scale heterogeneous environments. The framework incorporates edge computing, blockchain, Trusted Execution Environments (TEE), and Explainable AI (XAI) to improve security, transparency, and trust. A multi-layer architecture consisting of client devices, edge servers, federated coordinators, cloud services, and security modules is proposed. Adaptive client selection, weighted federated averaging, dynamic privacy allocation, and asynchronous synchronization are employed to improve learning under Non-IID data distributions. Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.

  • References

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