Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sample-based and Feature-based Federated Learning for Unconstrained and
  Constrained Nonconvex Optimization via Mini-batch SSCA   Federated learning (FL) has become a hot research area in enabling the
collaborative training of machine learning models among multiple clients that
hold sensitive local data. Nevertheless, unconstrained federated optimization
has been studied mainly using stochastic gradient descent (SGD), which may
converge slowly, and constrained federated optimization, which is more
challenging, has not been investigated so far. This paper investigates
sample-based and feature-based federated optimization, respectively, and
considers both unconstrained and constrained nonconvex problems for each of
them. First, we propose FL algorithms using stochastic successive convex
approximation (SSCA) and mini-batch techniques. These algorithms can adequately
exploit the structures of the objective and constraint functions and
incrementally utilize samples. We show that the proposed FL algorithms converge
to stationary points and Karush-Kuhn-Tucker (KKT) points of the respective
unconstrained and constrained nonconvex problems, respectively. Next, we
provide algorithm examples with appealing computational complexity and
communication load per communication round. We show that the proposed algorithm
examples for unconstrained federated optimization are identical to FL
algorithms via momentum SGD and provide an analytical connection between SSCA
and momentum SGD. Finally, numerical experiments demonstrate the inherent
advantages of the proposed algorithms in convergence speeds, communication and
computation costs, and model specifications.

22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat