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A Privacy Preservation Framework Using Integration of Blockchain and Federated Learning

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Abstract

Over the last few years, end devices’ rapid proliferation and development have placed obstacles on existing networks. New technologies have prompted attention to developing distributed and intelligent systems that protect data privacy. Federated Learning (FL) is a privacy-enhanced collaborative learning strategy that offers a model-based sharing mechanism rather than actual data to solve the hurdles of data privacy issues of participants’ data. This work suggested a blockchain-based FL system to replace the central server in conventional federated learning. We also verify the participants’ local model to detect the malicious model. This study examines the system performance by considering the participants’ IID and non-IID data distributions. To demonstrate the reliability of our suggested technique, we conducted an extensive experiment with popular datasets such as MNIST, EMNIST, CIFAR-10, and Fashion-MNIST.

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Data availability

Publicly available datasets are used during the current study and are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Computer Applications, Cochin University of Science and Technology, Ernakulam, Kochi, Kerala, 682022, India

    K. M. Sameera, K. A. Rafidha Rehiman & P. Vinod

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  1. K. M. Sameera
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  2. K. A. Rafidha Rehiman
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  3. P. Vinod
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Correspondence to K. A. Rafidha Rehiman.

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On behalf of all authors, the corresponding author states that there is no conflict of interest. The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

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This article is part of the topical collection “Soft Computing Solutions for Secured & Smart Applications” guest edited by Sridaran Rajagopal and Kalpesh Popat.

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Sameera, K.M., Rafidha Rehiman, K.A. & Vinod, P. A Privacy Preservation Framework Using Integration of Blockchain and Federated Learning. SN COMPUT. SCI. 4, 703 (2023). https://doi.org/10.1007/s42979-023-02075-7

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