<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si444.svg" display="inline" id="d1e3487"><mml:msup><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math>: Fair Federated Learning Framework with adaptive regularization

Abstract

In federated learning , ensuring high accuracy while maintaining fairness across heterogeneous clients presents a significant challenge. Existing approaches often fail to adequately balance these objectives, especially in non-IID environments. To address this issue, we propose an adaptive regularization framework, F , which dynamically adjusts the balance between global accuracy and fairness during training. Our method introduces two fairness metrics—variance and mean absolute deviation (MAD)—to quantify performance disparities among clients. By incorporating these metrics into the loss function, we enable adaptive tuning of the regularization parameter to maintain global performance while minimizing client imbalances. Extensive experiments across diverse datasets and heterogeneous environments demonstrate that our approach significantly improves both accuracy and fairness, outperforming baseline methods such as FedAvg and FairFed. These results highlight the potential of F to achieve a more balanced and robust federated learning system .

Topics & Concepts