In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate discriminatory outcomes in decision-making processes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of federated learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. One of the significant contributions of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the realm of fairness. In addition, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which utilizes a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine learning.

翻译：在机器学习的发展进程中，确保公平性已成为关键议题，催生了旨在减轻决策过程中歧视性结果的算法开发。然而，在分组特定概念漂移存在的情况下实现公平性仍是未探索的前沿领域，我们的研究在此方面开创了先河。分组特定概念漂移指的是，某个分组随时间经历概念漂移而另一分组未发生此现象，这将导致即使准确率保持相对稳定，公平性仍会下降。在联邦学习框架中（客户端协作训练模型），其分布式特性进一步放大了这些挑战：每个客户端可能独立经历分组特定概念漂移，同时又共享同一底层概念，从而为维护公平性创造了复杂且动态的环境。本研究的重要贡献之一在于正式定义并引入分组特定概念漂移问题及其分布式对应形式，揭示了该问题在公平性领域的至关重要的地位。此外，基于先前研究的洞见，我们适配了一种现有的分布式概念漂移适应算法以解决分组特定分布式概念漂移问题，该算法采用多模型方法、本地分组特定漂移检测机制以及随时间推移对模型进行持续聚类。实验结果表明，解决分组特定概念漂移及其分布式对应形式对推进机器学习公平性具有重要价值。