<ul><li>Federated learning involves training a global model from local models, but heterogeneous data distributions and data privacy pose challenges.</li><li>This study focuses on improving the adaptability of local models to enhance the performance of the global model.</li><li>The method introduces the concept of adaptability of local models and proposes a solution to optimize it without direct knowledge of other clients' data distributions.</li><li>Experimental results show that this approach boosts the adaptability of local models, leading to better overall performance compared to baseline methods.</li></ul>

Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning

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