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Fraunhofer Heinrich Hertz Institute, Fraunhofer Heinrich Hertz Institute BIFOLD Technical University Berlin
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Achieving formal privacy in federated learning without sacrificing model performance, FedKT-CSD outperforms traditional methods even under stringent privacy constraints.
Selective data sharing guided by XAI can significantly boost federated learning performance, achieving higher accuracy and faster convergence even in heterogeneous environments.
