Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Doubly Stochastic Kernel Learning for Vertically Partitioned
  Data   In a lot of real-world data mining and machine learning applications, data
are provided by multiple providers and each maintains private records of
different feature sets about common entities. It is challenging to train these
vertically partitioned data effectively and efficiently while keeping data
privacy for traditional data mining and machine learning algorithms. In this
paper, we focus on nonlinear learning with kernels, and propose a federated
doubly stochastic kernel learning (FDSKL) algorithm for vertically partitioned
data. Specifically, we use random features to approximate the kernel mapping
function and use doubly stochastic gradients to update the solutions, which are
all computed federatedly without the disclosure of data. Importantly, we prove
that FDSKL has a sublinear convergence rate, and can guarantee the data
security under the semi-honest assumption. Extensive experimental results on a
variety of benchmark datasets show that FDSKL is significantly faster than
state-of-the-art federated learning methods when dealing with kernels, while
retaining the similar generalization performance.

26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
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
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess