Federated learning (FL) has emerged as a new paradigm for privacy-preserving
collaborative training. Under domain skew, the current FL approaches are biased
and face two fairness problems. 1) Parameter Update Conflict: data disparity
among clients leads to varying parameter importance and inconsistent update
directions. These two disparities cause important parameters to potentially be
overwhelmed by unimportant ones of dominant updates. It consequently results in
significant performance decreases for lower-performing clients. 2) Model
Aggregation Bias: existing FL approaches introduce unfair weight allocation and
neglect domain diversity. It leads to biased model convergence objective and
distinct performance among domains. We discover a pronounced directional update
consistency in Federated Learning and propose a novel framework to tackle above
issues. First, leveraging the discovered characteristic, we selectively discard
unimportant parameter updates to prevent updates from clients with lower
performance overwhelmed by unimportant parameters, resulting in fairer
generalization performance. Second, we propose a fair aggregation objective to
prevent global model bias towards some domains, ensuring that the global model
continuously aligns with an unbiased model. The proposed method is generic and
can be combined with other existing FL methods to enhance fairness.
Comprehensive experiments on Digits and Office-Caltech demonstrate the high
fairness and performance of our method.

在研究中，我们提出了一个新的框架来解决分布式学习中的不公平问题，该框架利用发现的特性选择性地丢弃不重要的参数更新，并引入公平的聚合目标以确保全局模型的无偏性。验证实验证明了该方法在 Digits 和 Office-Caltech 数据集上的高公平性和性能表现。

领域偏差下的公平联邦学习：局部一致性与领域多样性

Fair Federated Learning under Domain Skew with Local Consistency and  Domain Diversity

Fair decision making has largely been studied with respect to a single
decision. In this paper we investigate the notion of fairness in the context of
sequential decision making where multiple stakeholders can be affected by the
outcomes of decisions, and where decision making may be informed by additional
constraints and criteria beyond the requirement of fairness. In this setting,
we observe that fairness often depends on the history of the sequential
decision-making process and not just on the current state. To advance our
understanding of this class of fairness problems, we define the notion of
non-Markovian fairness in the context of sequential decision making. We
identify properties of non-Markovian fairness, including notions of long-term,
anytime, periodic, and bounded fairness. We further explore the interplay
between non-Markovian fairness and memory, and how this can support
construction of fair policies in sequential decision-making settings.

非马尔科夫公平关系在顺序决策制定中起着重要作用，研究涉及多方利益相关者和附加约束条件的决策制定过程中的公平性问题。

记得公平：关于序列决策中的非马尔可夫公平（初步报告）

Remembering to Be Fair: On Non-Markovian Fairness in Sequential  DecisionMaking (Preliminary Report)

Fairness problems in recommender systems often have a complexity in practice
that is not adequately captured in simplified research formulations. A social
choice formulation of the fairness problem, operating within a multi-agent
architecture of fairness concerns, offers a flexible and multi-aspect
alternative to fairness-aware recommendation approaches. Leveraging social
choice allows for increased generality and the possibility of tapping into
well-studied social choice algorithms for resolving the tension between
multiple, competing fairness concerns. This paper explores a range of options
for choice mechanisms in multi-aspect fairness applications using both real and
synthetic data and shows that different classes of choice and allocation
mechanisms yield different but consistent fairness / accuracy tradeoffs. We
also show that a multi-agent formulation offers flexibility in adapting to user
population dynamics.

在社交选择公正问题的多代理体系结构下，研究探索了多方面公正应用中的多种选择机制，发现不同类别的选择和分配机制在公正 / 准确性权衡方面产生了不同但一致的结果，并展示多代理体系结构在适应用户人口动态方面具有灵活性。

探索 SCRUF 中用于推荐公平性的社会选择机制

Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF

Benefiting from the digitization of healthcare data and the development of
computing power, machine learning methods are increasingly used in the
healthcare domain. Fairness problems have been identified in machine learning
for healthcare, resulting in an unfair allocation of limited healthcare
resources or excessive health risks for certain groups. Therefore, addressing
the fairness problems has recently attracted increasing attention from the
healthcare community. However, the intersection of machine learning for
healthcare and fairness in machine learning remains understudied. In this
review, we build the bridge by exposing fairness problems, summarizing possible
biases, sorting out mitigation methods and pointing out challenges along with
opportunities for the future.

通过揭示机器学习应用在医疗保健中的公平性问题及其可能的偏差、整理相关缓解方法，本文旨在为建立医疗保健机器学习及相关技术的公正性提供参考。