Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Network Independent Rates in Distributed Learning   We propose a new belief update rule for Distributed Non-Bayesian learning in
time-varying directed graphs, where a group of agents tries to collectively
identify a hypothesis that best describes a sequence of observed data. We show
that the proposed update rule, inspired by the Push-Sum algorithm, is
consistent, moreover we provide an explicit characterization of its convergence
rate. Our main result states that, after a transient time, all agents will
concentrate their beliefs at a network independent rate. Network independent
rates were not available for other consensus based distributed learning
algorithms.

28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph