EquiConfig: Efficient Mitigation of Disparate Impact in Differentially Private Learning

Differentially private stochastic gradient descent (DP-SGD) enables neural network training with record-level privacy by clipping per-example gradients and adding calibrated noise to their aggregate. These operations can disproportionately weaken underrepresented groups' contributions, reducing their accuracy. Mitigation strategies rely on adaptive clipping and introduce extra parameters, increasing the computational and privacy costs of calibration. We propose EquiConfig, which screens DP-SGD's existing batch-size, clipping, and noise settings offline to construct a targeted shortlist for private calibration. Privacy accounting pairs each batch size with the noise required at the target privacy budget, forming a privacy ridge. Along each ridge, larger batches can strengthen group contributions relative to noise but leave fewer optimization steps under fixed privacy and epoch budgets. To assess this trade-off, EquiConfig defines a cumulative group-information score that combines per-update group signal relative to noise with expected group participation over training. Privacy-frontier elasticity analysis identifies batch increases that provably improve this score for every target group, while minimum total-update and group-activity requirements exclude configurations with too few training opportunities. We apply the screen to candidate grids spanning Gaussian, Laplace, and mixture-Laplace noise and multiple clipping bounds, yielding alternative configurations without adaptive training controls or gradient checkpoints. We compare against state-of-the-art mitigation methods both without private calibration and with DP-HyPO accounting for selection costs. Across vision, tabular, and language tasks, the results demonstrate substantial improvements in accuracy–disparity trade-offs at matched privacy targets, with fewer candidate evaluations under private calibration.