<ul><li>Federated Learning is gaining prominence due to rising data privacy concerns as it allows collaborative training without raw data aggregation.</li><li>Current focus in Federated Learning has been on parametric gradient-based models, with relatively less attention on nonparametric models like decision trees.</li><li>A new approach using Genetic Algorithm is explored in a recent study to create personalized decision trees that can handle categorical and numerical data for classification and regression tasks in Federated Learning.</li><li>Experiments show that this new approach outperforms traditional decision trees trained on local data as well as a benchmark algorithm.</li></ul>

FedGA-Tree: Federated Decision Tree using Genetic Algorithm

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