Fair Federated Learning under Group-Specific Distributed Concept Drift

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Machine learning models can become unfair when different groups experience changes in data over time, a phenomenon called group-specific concept drift. This challenge is amplified in Federated Learning, where clients may encounter different drifts while training a shared model. This talk introduces the problem and presents a distributed, group-aware approach that detects and adapts to such drifts, helping maintain fairness in dynamic environments.


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