Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Communication Efficient Distributed Learning with Censored, Quantized,
  and Generalized Group ADMM   In this paper, we propose a communication-efficiently decentralized machine
learning framework that solves a consensus optimization problem defined over a
network of inter-connected workers. The proposed algorithm, Censored and
Quantized Generalized GADMM (CQ-GGADMM), leverages the worker grouping and
decentralized learning ideas of Group Alternating Direction Method of
Multipliers (GADMM), and pushes the frontier in communication efficiency by
extending its applicability to generalized network topologies, while
incorporating link censoring for negligible updates after quantization. We
theoretically prove that CQ-GGADMM achieves the linear convergence rate when
the local objective functions are strongly convex under some mild assumptions.
Numerical simulations corroborate that CQ-GGADMM exhibits higher communication
efficiency in terms of the number of communication rounds and transmit energy
consumption without compromising the accuracy and convergence speed, compared
to the censored decentralized ADMM, and the worker grouping method of GADMM.

18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn