Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart
  Devices   Smart devices with built-in sensors, computational capabilities, and network
connectivity have become increasingly pervasive. The crowds of smart devices
offer opportunities to collectively sense and perform computing tasks in an
unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine
learning framework for a crowd of smart devices, which can solve a wide range
of learning problems for crowdsensing data with differential privacy
guarantees. Crowd-ML endows a crowdsensing system with an ability to learn
classifiers or predictors online from crowdsensing data privately with minimal
computational overheads on devices and servers, suitable for a practical and
large-scale employment of the framework. We analyze the performance and the
scalability of Crowd-ML, and implement the system with off-the-shelf
smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML
with real and simulated experiments under various conditions.

31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph