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Link to original content: https://unpaywall.org/10.1007/S11760-024-03081-9
Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective | Signal, Image and Video Processing Skip to main content
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Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective

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Abstract

As the Industrial Internet of Things (IIoT) continues to advance, ensuring robust data privacy in data aggregation processes becomes paramount. This paper introduces a novel approach, the federated recurrent-based adaptive battle royale (FR-ABR) algorithm, designed to address challenges such as latency, scalability, centralized aggregation, and data exposure in privacy-preserving data aggregation schemes within the IIoT. Leveraging federated learning, the FR-ABR algorithm employs Recurrent Neural Networks (RNNs) trained on decentralized edge devices, allowing each device to process local data without central data exposure. The study further integrates an adaptive strategy with battle royale optimization to fine-tune hyperparameters and enhance the overall performance of the FR-ABR method. Evaluation metrics including network lifetime, end-to-end delay, latency, throughput, energy consumption, communication overhead, and computation cost are employed to evaluate the performance of the FR-ABR method. Comparative analysis against existing methods establishes the superior performance of the FR-ABR algorithm, showcasing its potential as a robust solution for privacy-preserving data aggregation in IIoT applications. Experimental results presented in this study provide valuable insights into the efficacy and efficiency of the FR-ABR method within the realm of signal, image, and video processing for industrial networks.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Software and Systems Engineering, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    G. Jagadeesh, M. Pounambal & J. Vellingiri

  2. Department of Information Technology, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    K. ArivuSelvan & E. Sathiyamoorthy

  3. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    V. Madhu Viswanatham

  4. School of Creative Technologies, University of Bolton, Bolton, UK

    Celestine Iwendi

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  1. G. Jagadeesh
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  2. M. Pounambal
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  3. K. ArivuSelvan
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  4. E. Sathiyamoorthy
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  5. J. Vellingiri
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  7. Celestine Iwendi
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Contributions

All agreed on the content of the study. JG, MP, AK, ES, JV, MVV, and CI collected all the data for analysis. JG, MP, AK, ES, JV, MVV, and CI agreed on the methodology. JG, MP, AK, ES, JV, MVV, and CI completed the analysis based on agreed steps. Results and conclusions are discussed and written together. All authors read and approved the final manuscript.

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Correspondence to G. Jagadeesh.

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Jagadeesh, G., Pounambal, M., ArivuSelvan, K. et al. Federated recurrent-based adaptive battle royale algorithm for privacy-preserving data aggregation in industrial IoT: a signal, image, and video processing perspective. SIViP 18, 4395–4406 (2024). https://doi.org/10.1007/s11760-024-03081-9

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