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cs.LG2025
SoftAdaClip: A Smooth Clipping Strategy for Fair and Private Model Training
Dorsa Soleymani, Ali Dadsetan, Frank Rudzicz
Differential privacy (DP) provides strong protection for sensitive data, but often reduces model performance and fairness, especially for underrepresented groups. One major reason…
cs.LG2025
Re-examining Low Rank adaptation for private LLM fine-tuning
Ali Dadsetan, Frank Rudzicz
Privacy is a central concern when fine-tuning large language models (LLMs) on sensitive data, and differentially private stochastic gradient descent (DP-SGD) -- which clips per-sam…
cs.LG2024
Can large language models be privacy preserving and fair medical coders?
Ali Dadsetan, Dorsa Soleymani, Xijie Zeng +1
Protecting patient data privacy is a critical concern when deploying machine learning algorithms in healthcare. Differential privacy (DP) is a common method for preserving privacy…