Summary
The paper argue that the efficiency and data privacy of Federated Learning are non-orthogonal from the perspective of model training, which means they are restricting each other. So that the paper strictly formulates the problem at first, and designs a cloud-edge-end hierarchical FL system with adaptive control algorithm embedding a two-level Differential Protection method to relieve both the resource and privacy concerns. The design follows the following ideas: