A. Alzahrani

Revisiting Differentially Private Federated Learning for Tabular Data: A Matched-Accounting Benchmark of Boosting Versus DP-SGD

Gradient-boosted trees outperform neural networks on tabular data without privacy and off-path privatization and sequential noise accumulation explain the behavior; boosting’s main advantage is not accuracy but communication, achieving one to three orders of magnitude fewer values per client.