Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PrivFL: Practical Privacy-preserving Federated Regressions on
  High-dimensional Data over Mobile Networks   Federated Learning (FL) enables a large number of users to jointly learn a
shared machine learning (ML) model, coordinated by a centralized server, where
the data is distributed across multiple devices. This approach enables the
server or users to train and learn an ML model using gradient descent, while
keeping all the training data on users' devices. We consider training an ML
model over a mobile network where user dropout is a common phenomenon. Although
federated learning was aimed at reducing data privacy risks, the ML model
privacy has not received much attention.
  In this work, we present PrivFL, a privacy-preserving system for training
(predictive) linear and logistic regression models and oblivious predictions in
the federated setting, while guaranteeing data and model privacy as well as
ensuring robustness to users dropping out in the network. We design two
privacy-preserving protocols for training linear and logistic regression models
based on an additive homomorphic encryption (HE) scheme and an aggregation
protocol. Exploiting the training algorithm of federated learning, at the core
of our training protocols is a secure multiparty global gradient computation on
alive users' data. We analyze the security of our training protocols against
semi-honest adversaries. As long as the aggregation protocol is secure under
the aggregation privacy game and the additive HE scheme is semantically secure,
PrivFL guarantees the users' data privacy against the server, and the server's
regression model privacy against the users. We demonstrate the performance of
PrivFL on real-world datasets and show its applicability in the federated
learning system.

17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat