VerifyNet: Secure and Verifiable Federated Learning

As an emerging training model with neural networks, federated learning has received widespread attention due to its ability to update parameters without collecting users' raw data. However, since adversaries can track and derive participants' privacy from the shared gradients, federated learning is still exposed to various security and privacy threats. In this paper, we consider two major issues in the training process over deep neural networks (DNNs): 1) how to protect user's privacy (i.e., local gradients) in the training process and 2) how to verify the integrity (or correctness) of the aggregated results returned from the server. To solve the above problems, several approaches focusing on secure or…

• NS
  National Science Foundation
• NN
  National Natural Science Foundation of China

  Awards: 61802051, 61728102, 61772121, 61472065
• NK
  National Key Research and Development Program of China

  Awards: 2017YFB0802300, 2017YFB0802000