Federated learning is a promising approach for training machine models using distributed data from multiple mobile devices. However, privacy concerns arise when sensitive are used training. In this paper, we discuss the challenges of applying local differential to federated learning, which compounded by limited resources clients and asynchronicity learning. To address these challenges, propose ...