Blockchain-Enabled Federated Learning to Enhance Security and Privacy in Internet of Medical Things (IoMT)
Publish place: International Journal of Web Research، Vol: 6، Issue: 1
Publish Year: 1402
Type: Journal paper
Language: English
View: 187
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Document National Code:
JR_IJWR-6-1_010
Index date: 31 January 2024
Blockchain-Enabled Federated Learning to Enhance Security and Privacy in Internet of Medical Things (IoMT) abstract
Federated learning is a distributed data analysis approach used in many IoT applications, including IoMT, due to its ability to provide acceptable accuracy and privacy. However, a critical issue with Federated learning is the poisoning attack, which has severe consequences on the accuracy of the global model caused by the server's lack of access to raw data. To deal with this problem effectively, a distributed federated learning approach involving blockchain technology is proposed. Using the consensus mechanism based on reputation-based verifier selection, verifiers are selected based on their honest participation in identifying compromised clients. This approach ensures that these clients are correctly identified and their attack is ineffective. The proposed detection mechanism can efficiently resist the data poisoning attack, which significantly improves the accuracy of the global model. Based on evaluation, the accuracy of the global model is compared with and without the proposed detection mechanism that varies with the percentage of poisonous clients and different values for the fraction of poisonous data. In addition to the stable accuracy range of nearly 93%, the accuracy of our proposed detection mechanism is not affected by the increase of α in different values of β.
Blockchain-Enabled Federated Learning to Enhance Security and Privacy in Internet of Medical Things (IoMT) Keywords:
Blockchain , Consensus Algorithm , Federated Learning , Internet of Medical Things , Poisoning Attack
Blockchain-Enabled Federated Learning to Enhance Security and Privacy in Internet of Medical Things (IoMT) authors
zahra eskandari
Department of computer engineering , Quchan University of Technology, Quchan, Iran
Mohammad Rezaee
Department of computer engineering, Quchan University of Technology, quchan, Iran
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