Deep learning has shown incredible potential across a vast array of tasks and
accompanying this growth has been an insatiable appetite for data. However, a
large amount of data needed for enabling deep learning is stored on personal
devices and recent concerns on privacy have further highlighted challenges for
accessing such data. As a result, federated learning (FL) has emerged as an
important privacy-preserving technology enabling collaborative training of
machine learning models without the need to send the raw, potentially
sensitive, data to a central server. However, the fundamental premise that
sending model updates to a server is privacy-preserving only holds if the
updates cannot be "reverse engineered" to infer information about the private
training data. It has been shown under a wide variety of settings that this
premise for privacy does {\em not} hold.
In this survey paper, we provide a comprehensive literature review of the
different privacy attacks and defense methods in FL. We identify the current
limitations of these attacks and highlight the settings in which FL client
privacy can be broken. We dissect some of the successful industry applications
of FL and draw lessons for future successful adoption. We survey the emerging
landscape of privacy regulation for FL. We conclude with future directions for
taking FL toward the cherished goal of generating accurate models while
preserving the privacy of the data from its participants.

深度学习和联邦学习是当前研究的重点，其中涉及隐私保护技术、隐私攻击以及隐私法规的探讨。

联邦学习隐私：攻击、防御、应用和政策环境 - 一项调研

Federated Learning Privacy: Attacks, Defenses, Applications, and Policy  Landscape - A Survey

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.

为了保护数据隐私并解决联邦学习中的不公平问题，本文提出了一种基于无领域特定语言的去偏差联邦学习框架（DBFed），并通过实验验证其在准确性和公平性等方面的优越性。