Transfer learning plays a crucial role in adapting foundation models to downstream tasks. However, the fine-tuning process can be costly and raise privacy concerns due to the need to share data with model owners. To address this issue, cost-effective transfer learning techniques can be employed to reduce the amount of necessary data and make the process more efficient. These techniques include knowledge distillation, domain adaptation, and model weight interpolation, among others. By utilizing these methods, users can effectively adapt models without compromising privacy or breaking the bank.