Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning
  Using Shared Data on the Server   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).

19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex