Document Type

Conference Paper

Publication Date

2025

DOI

10.1145/3789982.3790038

Publication Title

Proceedings of the 2025 8th Artificial Intelligence and Cloud Computing Conference (AICCC 2025)

Pages

430-437

Conference Name

2025 8th Artificial Intelligence and Cloud Computing Conference (AICCC 2025), December 20-22, 2025, Tokyo Japan

Abstract

With the fast development and deep penetration of IoT devices and smart environments, using localized machine learning models to detect malicious activities has also been developed and deployed. However, these isolated learning models and results cannot be effectively federated together because of privacy concerns and lack of incentivization. This paper proposed several mechanisms to solve the problem. A verification method was designed for phased learning results to protect user privacy and prevent individual parties from manipulating the verification selection. The paper also presented an incentive method based on delay of distribution of the latest federated learning results. Extensive simulations were conducted to show the results of the approaches. The proposed approaches can achieve a balance between user privacy and data integrity, and provide incentives for collaboration in IoT environment security enforcement.

Rights

© 2025 Copyright held by the owner/authors.

This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Wang, W., Alam, M. M., & Wang, Y. (2025). Privacy-preserved and incentivized knowledge sharing for reinforced-learning based IoT platform security. In Proceedings of the 2025 8th Artificial Intelligence and Cloud Computing Conference (AICCC 2025) (pp. 430-437). Association for Computing Machinery. https://doi.org/10.1145/3789982.3790038

ORCID

0000-0001-8207-7930 (Alam)

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