Federated Stochastic Primal-dual Learning with Differential Privacy

Understand

Federated learning (FL) is a new paradigm that enables many clients to jointly train a machine learning (ML) model under the orchestration of a parameter server while keeping the local data not being exposed to any third party.

• However, the training of FL is an interactive process between local clients and the parameter server.
• Such process would cause privacy leakage since adversaries may retrieve sensitive information by analyzing the overheard messages.
• In this paper, we propose a new federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP).