FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction
Differentially private (DP) training protects individual examples by adding noise to gradients, but the injected noise interacts nontrivially with adaptive optimizers. Recent DP methods temporally filter privatized gradients to reduce variance; however, filtering also changes the DP noise statistics seen by AdamW's second-moment accumulator. As a result, bias corrections derived for unfiltered DP noise, such as subtracting sigma_w squared, can become miscalibrated when filtering is present. We propose FiBeR, a DP optimizer designed for temporally filtered privatized gradients. FiBeR (i) performs denoising in innovation space by filtering the residual stream and integrating it to form the ...
| Authors | Duc Dm, Thao Do, Minh Son Hoang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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