标题： 通过函数跟踪实现联合学习中的全局组公平性 显示英文标题

标题： Global Group Fairness in Federated Learning via Function Tracking

摘要： 我们研究了联邦学习中的群体公平正则化，旨在分布式环境中训练一个全局公平的模型。 在分布式训练中确保全局公平性提出了独特的挑战，因为公平正则化通常涉及所有客户端之间分布的概率度量，并且不能自然地按客户端分离。 为了解决这个问题，我们基于最大均值差异（MMD）引入了一种用于全局公平正则化的函数跟踪方案，该方案产生的通信开销很小。 此方案可以无缝集成到大多数联邦学习算法中，同时保持严格的收敛保证，如 FedAvg 的上下文中所展示的那样。 此外，在强制执行差分隐私时，基于核的 MMD 正则化通过核的改变可以轻松分析，利用了核卷积的直观解释。 数值实验验证了我们的理论见解。 显示英文摘要

摘要： We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probability metrics between distributions across all clients and are not naturally separable by client. To address this, we introduce a function-tracking scheme for the global fairness regularizer based on a Maximum Mean Discrepancy (MMD), which incurs a small communication overhead. This scheme seamlessly integrates into most federated learning algorithms while preserving rigorous convergence guarantees, as demonstrated in the context of FedAvg. Additionally, when enforcing differential privacy, the kernel-based MMD regularization enables straightforward analysis through a change of kernel, leveraging an intuitive interpretation of kernel convolution. Numerical experiments confirm our theoretical insights.