  As digital transformation continues, enterprises are generating, managing,
and storing vast amounts of data, while artificial intelligence technology is
rapidly advancing. However, it brings challenges in information security and
data security. Data security refers to the protection of digital information
from unauthorized access, damage, theft, etc. throughout its entire life cycle.
With the promulgation and implementation of data security laws and the emphasis
on data security and data privacy by organizations and users,
Privacy-preserving technology represented by federated learning has a wide
range of application scenarios. Federated learning is a distributed machine
learning computing framework that allows multiple subjects to train joint
models without sharing data to protect data privacy and solve the problem of
data islands. However, the data among multiple subjects are independent of each
other, and the data differences in quality may cause fairness issues in
federated learning modeling, such as data bias among multiple subjects,
resulting in biased and discriminatory models. Therefore, we propose DBFed, a
debiasing federated learning framework based on domain-independent, which
mitigates model bias by explicitly encoding sensitive attributes during
client-side training. This paper conducts experiments on three real datasets
and uses five evaluation metrics of accuracy and fairness to quantify the
effect of the model. Most metrics of DBFed exceed those of the other three
comparative methods, fully demonstrating the debiasing effect of DBFed.
