Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Learning via Inexact ADMM   One of the crucial issues in federated learning is how to develop efficient
optimization algorithms. Most of the current ones require full devices
participation and/or impose strong assumptions for convergence. Different from
the widely-used gradient descent-based algorithms, this paper develops an
inexact alternating direction method of multipliers (ADMM), which is both
computation and communication-efficient, capable of combating the stragglers'
effect, and convergent under mild conditions.

31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an