揭示特定群体的分布式概念漂移：联邦学习中的公平性要求
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
AI总结：
本研究首次提出并形式化了联邦学习中的特定群体分布式概念漂移问题，通过改编多模型分布式漂移适应算法来应对该挑战，实验证明解决此问题对推进机器学习公平性至关重要。
摘要：
在机器学习不断发展的领域中，确保群体公平性已成为一个关键问题，促使人们开发旨在减轻决策过程中偏见的算法。群体公平性是指模型决策应在由性别或种族等敏感属性定义的不同群体之间保持公平的原则，确保来自特权群体和非特权群体的个体受到公平对待并获得相似的结果。然而，在存在特定群体概念漂移的情况下实现公平仍是一个未探索的领域，我们的研究代表了这方面的开创性努力。特定群体概念漂移是指一个群体随时间经历概念漂移而另一个群体没有经历的情况，这导致即使准确率保持相当稳定，公平性也会下降。在联邦学习框架内，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，因为每个客户端可以独立经历特定群体概念漂移，同时仍然共享相同的基础概念，为维持公平性创造了复杂且动态的环境。我们研究最重要的贡献是形式化并引入了特定群体概念漂移及其分布式对应物的问题，阐明了其在公平性领域的关键重要性。此外，利用先前研究的见解，我们改编了一种现有的分布式概念漂移适应算法来应对特定群体分布式概念漂移，该算法使用多模型方法、本地特定群体漂移检测机制以及随时间对模型进行连续聚类。我们的实验结果突出了解决特定群体概念漂移及其分布式对应物以推进机器学习公平性的重要性。
英文摘要：
In the evolving field of machine learning, ensuring group fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. Group fairness refers to the principle that a model's decisions should be equitable across different groups defined by sensitive attributes such as gender or race, ensuring that individuals from privileged groups and unprivileged groups are treated fairly and receive similar outcomes. 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. The most significant contribution 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 field of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses 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.
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