Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mitigating Bias in Federated Learning   As methods to create discrimination-aware models develop, they focus on
centralized ML, leaving federated learning (FL) unexplored. FL is a rising
approach for collaborative ML, in which an aggregator orchestrates multiple
parties to train a global model without sharing their training data. In this
paper, we discuss causes of bias in FL and propose three pre-processing and
in-processing methods to mitigate bias, without compromising data privacy, a
key FL requirement. As data heterogeneity among parties is one of the
challenging characteristics of FL, we conduct experiments over several data
distributions to analyze their effects on model performance, fairness metrics,
and bias learning patterns. We conduct a comprehensive analysis of our proposed
techniques, the results demonstrating that these methods are effective even
when parties have skewed data distributions or as little as 20% of parties
employ the methods.

3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph