Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Decentralized Online Learning: Take Benefits from Others' Data without
  Sharing Your Own to Track Global Trend   Decentralized Online Learning (online learning in decentralized networks)
attracts more and more attention, since it is believed that Decentralized
Online Learning can help the data providers cooperatively better solve their
online problems without sharing their private data to a third party or other
providers. Typically, the cooperation is achieved by letting the data providers
exchange their models between neighbors, e.g., recommendation model. However,
the best regret bound for a decentralized online learning algorithm is
$\Ocal{n\sqrt{T}}$, where $n$ is the number of nodes (or users) and $T$ is the
number of iterations. This is clearly insignificant since this bound can be
achieved \emph{without} any communication in the networks. This reminds us to
ask a fundamental question: \emph{Can people really get benefit from the
decentralized online learning by exchanging information?} In this paper, we
studied when and why the communication can help the decentralized online
learning to reduce the regret. Specifically, each loss function is
characterized by two components: the adversarial component and the stochastic
component. Under this characterization, we show that decentralized online
gradient (DOG) enjoys a regret bound $\Ocal{n\sqrt{T}G + \sqrt{nT}\sigma}$,
where $G$ measures the magnitude of the adversarial component in the private
data (or equivalently the local loss function) and $\sigma$ measures the
randomness within the private data. This regret suggests that people can get
benefits from the randomness in the private data by exchanging private
information. Another important contribution of this paper is to consider the
dynamic regret -- a more practical regret to track users' interest dynamics.
Empirical studies are also conducted to validate our analysis.

24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph