Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Resource-Aware Asynchronous Online Federated Learning for Nonlinear
  Regression   Many assumptions in the federated learning literature present a best-case
scenario that can not be satisfied in most real-world applications. An
asynchronous setting reflects the realistic environment in which federated
learning methods must be able to operate reliably. Besides varying amounts of
non-IID data at participants, the asynchronous setting models heterogeneous
client participation due to available computational power and battery
constraints and also accounts for delayed communications between clients and
the server. To reduce the communication overhead associated with asynchronous
online federated learning (ASO-Fed), we use the principles of
partial-sharing-based communication. In this manner, we reduce the
communication load of the participants and, therefore, render participation in
the learning task more accessible. We prove the convergence of the proposed
ASO-Fed and provide simulations to analyze its behavior further. The
simulations reveal that, in the asynchronous setting, it is possible to achieve
the same convergence as the federated stochastic gradient (Online-FedSGD) while
reducing the communication tenfold.

37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin