Achieving fairness across diverse clients in Federated Learning (FL) remains
a significant challenge due to the heterogeneity of the data and the
inaccessibility of sensitive attributes from clients' private datasets. This
study addresses this issue by introducing \texttt{EquiFL}, a novel approach
designed to enhance both local and global fairness in federated learning
environments. \texttt{EquiFL} incorporates a fairness term into the local
optimization objective, effectively balancing local performance and fairness.
The proposed coordination mechanism also prevents bias from propagating across
clients during the collaboration phase. Through extensive experiments across
multiple benchmarks, we demonstrate that \texttt{EquiFL} not only strikes a
better balance between accuracy and fairness locally at each client but also
achieves global fairness. The results also indicate that \texttt{EquiFL}
ensures uniform performance distribution among clients, thus contributing to
performance fairness. Furthermore, we showcase the benefits of \texttt{EquiFL}
in a real-world distributed dataset from a healthcare application, specifically
in predicting the effects of treatments on patients across various hospital
locations.

通过引入 EquiFL，增强联邦学习环境中的本地和全局公平性，同时确保准确性和公平性之间的良好平衡，并通过协调机制防止偏见在合作阶段间的传播。在多个基准测试中进行了广泛的实验证明 EquiFL 不仅在每个客户端实现了准确性和公平性之间更好的平衡，还实现了全局公平性，并确保了客户端之间的性能公平分布。此外，我们还展示了 EquiFL 在一个真实的分布式医疗应用数据集中的好处，特别是在预测各个医院地点对患者治疗效果方面。

在联邦学习中实现本地模型和全局模型的公平性

Achieving Fairness Across Local and Global Models in Federated Learning

Federated Learning (FL) is a decentralized machine learning (ML) approach
that keeps data localized and often incorporates Differential Privacy (DP) to
enhance privacy guarantees. Similar to previous work on DP in ML, we observed
that differentially private federated learning (DPFL) introduces performance
disparities, particularly affecting minority groups. Recent work has attempted
to address performance fairness in vanilla FL through clustering, but this
method remains sensitive and prone to errors, which are further exacerbated by
the DP noise in DPFL. To fill this gap, in this paper, we propose a novel
clustered DPFL algorithm designed to effectively identify clients' clusters in
highly heterogeneous settings while maintaining high accuracy with DP
guarantees. To this end, we propose to cluster clients based on both their
model updates and training loss values. Our proposed approach also addresses
the server's uncertainties in clustering clients' model updates by employing
larger batch sizes along with Gaussian Mixture Model (GMM) to alleviate the
impact of noise and potential clustering errors, especially in
privacy-sensitive scenarios. We provide theoretical analysis of the
effectiveness of our proposed approach. We also extensively evaluate our
approach across diverse data distributions and privacy budgets and show its
effectiveness in mitigating the disparate impact of DP in FL settings with a
small computational cost.

提出了一种新颖的聚类差分私有化联邦学习算法，通过基于模型更新和训练损失值对客户进行聚类，以应对联邦学习中的不平等性和隐私保护问题。