DP-LAC provides a new adaptive clipping technique for DP-SGD in federated LLM fine-tuning that improves accuracy by 6.6% on average without consuming additional privacy budget or requiring new hyperparameters.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
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DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
DP-LAC provides a new adaptive clipping technique for DP-SGD in federated LLM fine-tuning that improves accuracy by 6.6% on average without consuming additional privacy budget or requiring new hyperparameters.