Cited by 125Privacy-Preserving Technologies in DataBlockchain Technology Applications and SecurityAdversarial Robustness in Machine Learning

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Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems

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Kalapaaking, Aditya Pribadi, Khalil, Ibrahim, Yi, Xun·2024·IEEE Transactions on Emerging Topics in Computing

DOI 10.1109/tetc.2023.3268186

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Abstract

Due to the rising awareness of privacy and security in machine learning applications, federated learning (FL) has received widespread attention and applied to several areas, e.g., intelligence healthcare systems, IoT-based industries, and smart cities. FL enables clients to train a global model collaboratively without accessing their local training data. However, the current FL schemes are vulnerable to adversarial attacks. Its architecture makes detecting and defending against malicious model updates difficult. In addition, most recent studies to detect FL from malicious updates while maintaining the model's privacy have not been sufficiently explored. This paper proposed blockchain-based federated learning with SMPC model verification against poisoning attacks for healthcare systems. First, we check the machine learning model from the FL participants through an encrypted inference process and remove the compromised model. Once the participants' local models have been verified, the models are sent to the blockchain node to be securely aggregated. We conducted several experiments with different medical datasets to evaluate our proposed framework.

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Authors: Kalapaaking, A. P., Khalil, I., & Yi, X.
Affiliations: RMIT University (AU)
ORCID: Kalapaaking, Aditya Pribadi
Khalil, Ibrahim
Yi, Xun
Venue: IEEE Transactions on Emerging Topics in Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2024-01
Volume: 12
Issue: 1
Pages: 269-280
ISSN: 2168-6750 (ISSN-L)
2376-4562
DOI: 10.1109/tetc.2023.3268186
OpenAlex: W4366678696
Cited by: 122
References: 30
Citations by year: 2026: 34 · 2025: 45 · 2024: 28
FWCI: 11.49
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Emerging Topics in Computing (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Australian Research Council (DP210102761)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords: blockchain-based federated learning, secure multi-party computation, model verification, poisoning attacks, health care system, encrypted inference, privacy-preserving machine learning

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Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems

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