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：在机器学习领域不断发展中，确保公平性已成为一项关键挑战，推动着旨在减轻决策过程中歧视性结果的算法设计。然而，在存在群体特异性概念漂移的情况下实现公平性仍是一个未探索的前沿领域，我们的研究正是这一方面的开创性工作。群体特异性概念漂移指某一群体随时间经历概念漂移而另一群体未发生此变化的情形，即便准确率保持相对稳定，也会导致公平性下降。在联邦学习框架下，由于客户端协同训练模型，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群体特异性概念漂移，却仍共享相同的基础概念，从而形成维护公平性的复杂动态环境。本研究的重要贡献之一在于正式定义并引入了群体特异性概念漂移问题及其分布式变体，揭示了其在公平性领域的核心重要性。此外，基于先前研究的洞察，我们改进了一种现有分布式概念漂移自适应算法，采用多模型方法、局部群体特异性漂移检测机制以及模型随时间持续聚类策略，以应对群体特异性分布式概念漂移。实验结果表明，解决群体特异性概念漂移及其分布式变体对推进机器学习公平性至关重要。