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Title	Blockchain-Based Federated Learning With Smpc Model Verification Against Poisoning Attack For Healthcare Systems
ID_Doc	12609
Authors	Kalapaaking A.P.; Khalil I.; Yi X.
Year	2024
Published	IEEE Transactions on Emerging Topics in Computing, 12, 1
DOI	http://dx.doi.org/10.1109/TETC.2023.3268186
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 article 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. © 2013 IEEE.
Author Keywords	blockchain; encrypted inference; Federated learning; healthcare systems; poisoning attack; secure multi-party computation

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