  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.
