Federated Learning for Secure Data Sharing Across Distributed Networks

  1. Lawal G. Anand

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Abstract

Federated learning (FL) has emerged as a transformative paradigm for collaborative model training without the need to centralize sensitive information. By enabling multiple participants to train a shared model locally and only exchange model updates, FL preserves privacy while leveraging the diversity of distributed data. This approach is particularly significant in domains such as healthcare, finance, and industrial Internet of Things, where data confidentiality and compliance with regulatory standards are critical. Despite its promise, FL faces challenges related to security vulnerabilities, communication overhead, and model aggregation fairness across heterogeneous networks. Recent advances in secure aggregation, differential privacy, and blockchain integration have shown potential in mitigating these risks while ensuring trust among participants. This paper examines the role of federated learning as a mechanism for secure data sharing across distributed networks, highlighting its core advantages, limitations, and future directions for achieving scalable and resilient decentralized intelligence.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/17168725.

    Does the introduction explain the objective of the research presented in the preprint? Partly
    Are the methods well-suited for this research? Somewhat appropriate
    Are the conclusions supported by the data? Highly supported
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Somewhat clearly
    Is the preprint likely to advance academic knowledge? Somewhat likely
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, but it needs to be improved
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes

    Competing interests

    The author declares that they have no competing interests.

  2. Version published to 10.20944/preprints202509.0828.v1