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Federated learning incentivize with privacy-preserving for IoT in edge computing in the context of B5G

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Abstract

Federated learning and edge computing have resulted in the broad adoption of internet of Things (IoT) due to their fast reaction times and low connection costs. In general, edge computing requires users to send raw data to a central server for further processing. However, this data frequently contains sensitive information that individuals may not want to disclose. As a result, transferring user data with sensitive information increases the risk of data leakage when various unauthorized devices access it. Integrating federated learning with edge computing improves privacy by creating a consistent deep learning model across devices, eliminating the need for real data transferring but the complexity and heterogeneity of computational resources in the IoT environment present challenges like privacy, low communication, incentives, and seamless data aggregation. In this work, we concentrate on improving privacy with communication stability and designing incentive mechanisms to motivate more clients to participate in the model training process to enhance the performance and accuracy of data aggregation in a highly trusted environment. As a result, the novelty point is the development of a framework that combined a blockchain with federated edge computing in the context of beyond 5G to address the aforementioned challenges and provide excellent communicative and trusted environment. The study's rigorous evaluation showed that the integration of blockchain and B5G technology significantly improved the overall process of federated edge computing including increased accuracy, prevented loss, and motivated more clients to participate in the training process.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2023YFC3303800)

Funding

Funding was provided by the National Key Research and Development Program of China (2023YFC3303800).

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Authors and Affiliations

  1. Department of Computer Science, University of Science and Technology Beijing (USTB), Beijing, 100083, China

    Nasir Ahmad Jalali & Chen Hongsong

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  1. Nasir Ahmad Jalali
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  2. Chen Hongsong
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All authors contributed equally to the conceptualization and design of the solution for mentioned challenges. Data collection and analysis performed by Nasir Ahmad Jalali and Professor Chen Hong song provide supervision as well as reviewed the paper for quality improvement.

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Correspondence to Chen Hongsong.

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Jalali, N.A., Hongsong, C. Federated learning incentivize with privacy-preserving for IoT in edge computing in the context of B5G. Cluster Comput 28, 112 (2025). https://doi.org/10.1007/s10586-024-04788-7

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