Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System

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…

• NN
  National Natural Science Foundation of China

  Award: 61872138
• NS
  Natural Science Foundation of Hunan Province

  Award: 2021JJ30278