Transfer Learning Methods to Protect User Data

To address privacy concerns arising from sharing data with model owners for fine-tuning foundation models, transfer learning methods can be used as an alternative approach. This allows for the adaptation of pre-trained models to downstream tasks without sharing sensitive user data. Various transfer learning techniques exist, including domain adaptation and model distillation. These methods can help protect user privacy while still achieving high task performance.