Privacy-Preserving Online Mirror Descent With Single-Sided Trust for Federated Learning

Existing federated learning uses a central server prone to communication and computational bottlenecks. Also, most existing federated learning algorithms do not cater for situations where the data distribution is time-varying such as in real-time traffic monitoring. To address these problems, this paper proposes a novel differentially private online mirror descent algorithm. To provide additional privacy to the loss gradients of the clients, local differential privacy is introduced.