Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

TL;DR

This paper introduces the concept of group-specific distributed concept drift in federated learning, emphasizing its impact on fairness and proposing an adapted detection algorithm to mitigate bias caused by such drift.

Contribution

It formalizes the problem of group-specific concept drift in federated learning and adapts an existing algorithm to detect and address this challenge for improved fairness.

Findings

Addressing group-specific concept drift enhances fairness in federated learning.

The adapted algorithm effectively detects group-specific drift in experiments.

Mitigating drift maintains equitable outcomes across groups.

Abstract

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.