Homomorphic Encryption-based Privacy-preserving Federated Learning in IoT-enabled Healthcare System

Li Zhang, Jianbo Xu, Pandi Vijayakumar, Pradip Kumar Sharma, Uttam Ghosh

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In this work, the federated learning mechanism is introduced into the deep learning of medical models in Internet of Things (IoT)-based healthcare system. Cryptographic primitives, including masks and homomorphic encryption, are applied for further protecting local models, so as to prevent the adversary from inferring private medical data by various attacks such as model reconstruction attack or model inversion attack, etc. The qualities of the datasets owned by different participants are considered as the main factor for measuring the contribution rate of the local model to the global model in each training epoch, instead of the size of datasets commonly used in deep learning. A dropout-tolerable scheme is proposed in which the process of federated learning would not be terminated if the number of online clients is not less than a preset threshold. Through the analysis of the security, it shows that the proposed scheme satisfies data privacy. Computation cost and communication cost are also analyzed theoretically. Finally, skin lesion classification using training images provided by the HAM10000 medical dataset is set as an example of healthcare applications. Experimental results show that compared with existing schemes, the proposed scheme obtained promising results while ensuring privacy preserving.

Original languageEnglish
Number of pages17
JournalIEEE Transactions on Network Science and Engineering
Early online date30 Jun 2022
DOIs
Publication statusE-pub ahead of print - 30 Jun 2022

Keywords

  • Collaborative work
  • Computational modeling
  • Convolutional Neural Networks
  • Data models
  • Federated Learning
  • Homomorphic encryption
  • Homomorphic Encryption
  • IoT-enabled Healthcare System
  • Medical diagnostic imaging
  • Medical services
  • Privacy
  • Privacy-preserving

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