The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems
Open accessAbstract
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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In the citing paper
“SMPC allows edge devices to perform computations on their local models while maintaining privacy.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
In the citing paper
“In SMPC, each edge device i contributes its local model w i without revealing the parameters of the model or the data used for training.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
In the citing paper
“Secure protocols such as homomorphic encryption or secret sharing facilitate the computation of aggregate statistics, including gradients, on encrypted data.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
In the citing paper
“These methods aggregate updates without disclosing sensitive information to the central server or other parties, thus maintaining privacy.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
In the citing paper
“SMPC ensures that individual edge device data remain secret throughout the aggregation process, making it suitable for scenarios with strict privacy requirements [84].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
In the citing paper
“Techniques such as secure multi-party computation (SMPC) or homomorphic encryption ensure that local model updates remain confidential.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
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Citations
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Metadata
- Authors
- Kalapaaking, A. P., Khalil, I., & Yi, X.
- Affiliations
- RMIT University (AU)
- Venue
- IEEE Transactions on Emerging Topics in Computing
- Published
- 2024-01
- Volume
- 12
- Issue
- 1
- Pages
- 269-280
- ISSN
- 2168-6750 (ISSN-L)
- 2376-4562
- 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
- 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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