Corpus ID: 231728768

User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

@inproceedings{Wei2020UserLevelPF,
  title={User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization},
  author={Kang Wei and J. Li and M. Ding and Chuan Ma and Hang Su and B. Zhang and H. V. Poor},
  year={2020}
}
  • Kang Wei, J. Li, +4 authors H. V. Poor
  • Published 2020
  • Computer Science
  • Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. Nevertheless, from a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. To address this problem, we first make use of the concept of local differential privacy (LDP), and propose a user-level differential privacy… CONTINUE READING