<ul><li>This paper focuses on benchmarking machine unlearning methods for tabular data within a federated learning (FL) setting.</li><li>The study explores unlearning at the feature and instance levels using machine learning models.</li><li>The benchmarking methodology evaluates various unlearning algorithms and compares their fidelity, certifiability, and computational efficiency.</li><li>The results show that tree-based models excel in certifiability, while gradient-based methods offer improved computational efficiency.</li></ul>

Benchmarking Federated Machine Unlearning methods for Tabular Data

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