Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Learning Meets Fairness and Differential Privacy   Deep learning's unprecedented success raises several ethical concerns ranging
from biased predictions to data privacy. Researchers tackle these issues by
introducing fairness metrics, or federated learning, or differential privacy. A
first, this work presents an ethical federated learning model, incorporating
all three measures simultaneously. Experiments on the Adult, Bank and Dutch
datasets highlight the resulting ``empirical interplay" between accuracy,
fairness, and privacy.

21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas