Differentially Private Bias-Term only Fine-tuning of Foundation Models. (arXiv:2210.00036v1 [cs.LG])

We study the problem of differentially private (DP) fine-tuning of large
pre-trained models -- a recent privacy-preserving approach suitable for solving
downstream tasks with sensitive data. Existing work has demonstrated that high
accuracy is possible under strong privacy constraint, yet requires significant
computational overhead or modifications to the network architecture.

We propose differentially private bias-term fine-tuning (DP-BiTFiT), which
matches the state-of-the-art accuracy for DP algorithms and the efficiency of
the standard BiTFiT. DP-BiTFiT is model agnostic (not modifying the network …

bias foundation