  Despite achieving remarkable performance, Federated Learning (FL) suffers
from two critical challenges, i.e., limited computational resources and low
training efficiency. In this paper, we propose a novel FL framework, i.e.,
FedDUAP, with two original contributions, to exploit the insensitive data on
the server and the decentralized data in edge devices to further improve the
training efficiency. First, a dynamic server update algorithm is designed to
exploit the insensitive data on the server, in order to dynamically determine
the optimal steps of the server update for improving the convergence and
accuracy of the global model. Second, a layer-adaptive model pruning method is
developed to perform unique pruning operations adapted to the different
dimensions and importance of multiple layers, to achieve a good balance between
efficiency and effectiveness. By integrating the two original techniques
together, our proposed FL model, FedDUAP, significantly outperforms baseline
approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8
times faster), and computational cost (up to 61.9% smaller).
