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Despite achieving remarkable performance, Federated Learning (FL) suffersfrom two critical challenges, i.e., limited computational resources and lowtraining efficiency. In this paper, we propose a novel FL framework, i.e.,FedDUAP, with two original contributions, to exploit the insensitive data onthe server and the decentralized data in edge devices to further improve thetraining efficiency. First, a dynamic server update algorithm is designed toexploit the insensitive data on the server, in order to dynamically determinethe optimal steps of the server update for improving the convergence andaccuracy of the global model. Second, a layer-adaptive model pruning method isdeveloped to perform unique pruning operations adapted to the differentdimensions and importance of multiple layers, to achieve a good balance betweenefficiency and effectiveness. By integrating the two original techniquestogether, our proposed FL model, FedDUAP, significantly outperforms baselineapproaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8times faster), and computational cost (up to 61.9% smaller).

FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server