Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Coding and Scheduling Optimization for Distributed Learning over
  Wireless Edge Networks   Unlike theoretical distributed learning (DL), DL over wireless edge networks
faces the inherent dynamics/uncertainty of wireless connections and edge nodes,
making DL less efficient or even inapplicable under the highly dynamic wireless
edge networks (e.g., using mmW interfaces). This article addresses these
problems by leveraging recent advances in coded computing and the deep dueling
neural network architecture. By introducing coded structures/redundancy, a
distributed learning task can be completed without waiting for straggling
nodes. Unlike conventional coded computing that only optimizes the code
structure, coded distributed learning over the wireless edge also requires to
optimize the selection/scheduling of wireless edge nodes with heterogeneous
connections, computing capability, and straggling effects. However, even
neglecting the aforementioned dynamics/uncertainty, the resulting joint
optimization of coding and scheduling to minimize the distributed learning time
turns out to be NP-hard. To tackle this and to account for the dynamics and
uncertainty of wireless connections and edge nodes, we reformulate the problem
as a Markov Decision Process and then design a novel deep reinforcement
learning algorithm that employs the deep dueling neural network architecture to
find the jointly optimal coding scheme and the best set of edge nodes for
different learning tasks without explicit information about the wireless
environment and edge nodes' straggling parameters. Simulations show that the
proposed framework reduces the average learning delay in wireless edge
computing up to 66% compared with other DL approaches. The jointly optimal
framework in this article is also applicable to any distributed learning scheme
with heterogeneous and uncertain computing nodes.

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The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom