Corpus ID: 231582963

On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times

@article{Fu2021OnTP,
  title={On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times},
  author={Yao Fu and Y. Zhou and Di Wu and Shui Yu and Yonggang Wen and Chao Li},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.04163}
}
  • Yao Fu, Y. Zhou, +3 authors Chao Li
  • Published 2021
  • Computer Science
  • ArXiv
  • In spite that Federated Learning (FL) is well known for its privacy protection when training machine learning models among distributed clients collaboratively, recent studies have pointed out that the naive FL is susceptible to gradient leakage attacks. In the meanwhile, Differential Privacy (DP) emerges as a promising countermeasure to defend against gradient leakage attacks. However, the adoption of DP by clients in FL may significantly jeopardize the model accuracy. It is still an open… CONTINUE READING

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