<ul><li>With the emerging application of Federated Learning (FL) in finance, hiring, and healthcare, fairness is crucial to prevent disparities across legally protected attributes like race or gender.</li><li>Global fairness addresses the disparity across the entire population, while local fairness focuses on the disparity within each client.</li><li>This paper introduces a framework that investigates the minimum accuracy lost for enforcing specified levels of global and local fairness in multi-class FL settings.</li><li>Experimental results show that the proposed algorithm outperforms the current state of the art in terms of accuracy-fairness tradeoffs, computational costs, and communication costs.</li></ul>

The Cost of Local and Global Fairness in Federated Learning

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