Privacy aware-based federated learning framework for data sharing protection of internet of things devices

Saputra, Yuris Mulya and Alfian, Ganjar (2023) Privacy aware-based federated learning framework for data sharing protection of internet of things devices. Indonesian Journal of Electrical Engineering and Computer Science, 31 (2). 979 – 985. ISSN 25024752

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Abstract

Federated learning (FL) has emerged as one of the most effective solutions to deal with the rapid utilization of internet of things (IoT) in big data markets. Through FL, local data at each IoT device can be trained locally without sharing the local data to the cloud server. However, this conventional FL may still suffer from privacy leakage when the local data are trained, and the trained model is shared to the cloud server to update the global prediction model. This paper proposes a FL framework with privacy awareness to protect data including the trained model for IoT devices. First, a data/model encryption method using fully homomorphic encryption is introduced, aiming at protecting the data/model privacy. Then, the FL framework for the IoT with the encryption method leveraging logistic regression approach is discussed. Experimental results using random datasets show that the proposed framework can obtain higher global model accuracy (up to 4.84) and lower global model loss (up to 66.4) compared with other baseline methods. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Item Type: Article
Additional Information: Cited by: 1; All Open Access, Gold Open Access, Green Open Access
Uncontrolled Keywords: Artificial intelligence; Data privacy; Encryption; Federated learning; Internet of things
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Vocational School
Depositing User: Sri JUNANDI
Date Deposited: 03 Nov 2024 21:38
Last Modified: 03 Nov 2024 21:38
URI: https://ir.lib.ugm.ac.id/id/eprint/10546

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