A Balancing Act: Privacy Constrained Fine-tuning Optimizations The fine-tuning phase in DP pipelining has observed a tussle between strategies, where straightforward full fine-tuning doesn’t always cinch the best accuracy. This paper, On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune?, unravels the conundrums of training dynamics in DP, exposing the subtle nuances of allocating privacy budgets between linear probing and full fine-tuning for optimizing test loss.

Essential Insights:

• Offers a theoretical framework for the convergence of DP fine-tuning within overparameterized neural networks.
• Empirical support for the utility curve dictating privacy budget allocation.

Relevance and Horizons:

A deep dive into DP methods enriches our understanding of privacy-aware ML, underscored by the cardinal need to adjust privacy allocations wisely. This insight could be pivotal for organizations working under stringent regulatory frameworks, paving a path for responsible AI advancements.