Analysis of Differentially-Private Fine-Tuning Strategies
The author explores the convergence of differentially-private fine-tuning methods, highlighting the superiority of a sequential approach combining linear probing and full fine-tuning. Theoretical insights and empirical evaluations reveal the complexity and importance of privacy budget allocation in the fine-tuning process.
