Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Asynchronous Upper Confidence Bound Algorithms for Federated Linear
  Bandits   Linear contextual bandit is a popular online learning problem. It has been
mostly studied in centralized learning settings. With the surging demand of
large-scale decentralized model learning, e.g., federated learning, how to
retain regret minimization while reducing communication cost becomes an open
challenge. In this paper, we study linear contextual bandit in a federated
learning setting. We propose a general framework with asynchronous model update
and communication for a collection of homogeneous clients and heterogeneous
clients, respectively. Rigorous theoretical analysis is provided about the
regret and communication cost under this distributed learning framework; and
extensive empirical evaluations demonstrate the effectiveness of our solution.

18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th