Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A communication efficient distributed learning framework for smart
  environments   Due to the pervasive diffusion of personal mobile and IoT devices, many
``smart environments'' (e.g., smart cities and smart factories) will be, among
others, generators of huge amounts of data. Currently, this is typically
achieved through centralised cloud-based data analytics services. However,
according to many studies, this approach may present significant issues from
the standpoint of data ownership, and even wireless network capacity. One
possibility to cope with these shortcomings is to move data analytics closer to
where data is generated. In this paper, we tackle this issue by proposing and
analyzing a distributed learning framework, whereby data analytics are
performed at the edge of the network, i.e., on locations very close to where
data is generated. Specifically, in our framework, partial data analytics are
performed directly on the nodes that generate the data, or on nodes close by
(e.g., some of the data generators can take this role on behalf of subsets of
other nodes nearby). Then, nodes exchange partial models and refine them
accordingly. Our framework is general enough to host different analytics
services. In the specific case analysed in the paper, we focus on a learning
task, considering two distributed learning algorithms. Using an activity
recognition and a pattern recognition task, both on reference datasets, we
compare the two learning algorithms between each other and with a central cloud
solution (i.e., one that has access to the complete datasets). Our results show
that using distributed machine learning techniques, it is possible to
drastically reduce the network overhead, while obtaining performance comparable
to the cloud solution in terms of learning accuracy. The analysis also shows
when each distributed learning approach is preferable, based on the specific
distribution of the data on the nodes.

22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat