Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantized Epoch-SGD for Communication-Efficient Distributed Learning   Due to its efficiency and ease to implement, stochastic gradient descent
(SGD) has been widely used in machine learning. In particular, SGD is one of
the most popular optimization methods for distributed learning. Recently,
quantized SGD (QSGD), which adopts quantization to reduce the communication
cost in SGD-based distributed learning, has attracted much attention. Although
several QSGD methods have been proposed, some of them are heuristic without
theoretical guarantee, and others have high quantization variance which makes
the convergence become slow. In this paper, we propose a new method, called
Quantized Epoch-SGD (QESGD), for communication-efficient distributed learning.
QESGD compresses (quantizes) the parameter with variance reduction, so that it
can get almost the same performance as that of SGD with less communication
cost. QESGD is implemented on the Parameter Server framework, and empirical
results on distributed deep learning show that QESGD can outperform other
state-of-the-art quantization methods to achieve the best performance.

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The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
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
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an