reapxdev/zenodo-scraper huggingface dataset:
Revisiting Differentially Private Federated Learning for Tabular Data: A Matched-Accounting Benchmark of Boosting versus DP-SGD
Authors: Alzahrani, Ahmed
Type: Software
Published: 2026-08-03
Keywords: differential privacy, federated learning, tabular data, gradient boosting, DP-SGD, privacy accounting, benchmark

Source code, experimental configurations, privacy-accounting parameters, and result logs for the matched-accounting benchmark comparing differentially private gradient boosting against DP-SGD for federated learning on tabular data. Both method families are calibrated with the same privacy accountant to identical (epsilon, delta) budgets. Version 1.1.0 adds the major-revision analysis suite: the optimal-round-count (T*) theory simulation under varying delta and sampling rate q, the loss-threshold membership-inference experiment, the heterogeneity/participation/covariate-shift ablations, the runtime-memory-latency profiling, and a DP random-forest baseline under parallel composition, with all result logs and 300-DPI figures.Version 1.2.0 adds the reference benchmark harness for the real corpora with per-table configurations.