Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Learning with Nesterov Accelerated Gradient Momentum Method   Federated learning (FL) is a fast-developing technique that allows multiple
workers to train a global model based on a distributed dataset. Conventional FL
employs gradient descent algorithm, which may not be efficient enough. It is
well known that Nesterov Accelerated Gradient (NAG) is more advantageous in
centralized training environment, but it is not clear how to quantify the
benefits of NAG in FL so far. In this work, we focus on a version of FL based
on NAG (FedNAG) and provide a detailed convergence analysis. The result is
compared with conventional FL based on gradient descent. One interesting
conclusion is that as long as the learning step size is sufficiently small,
FedNAG outperforms FedAvg. Extensive experiments based on real-world datasets
are conducted, verifying our conclusions and confirming the better convergence
performance of FedNAG.

36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int