Empirical study finds small client batches most vulnerable to gradient inversion in tabular FL, FT-Transformer harder to invert than MLP baselines, and aggregate accuracy metrics can overstate exact record recovery.
Prac- tical feasibility of gradient inversion attacks in federated learning
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Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
Empirical study finds small client batches most vulnerable to gradient inversion in tabular FL, FT-Transformer harder to invert than MLP baselines, and aggregate accuracy metrics can overstate exact record recovery.