<ul data-eligibleForWebStory="false">Federated finetuning combines federated learning and smart techniques to preserve privacy while improving large-scale models.Organizations can train models locally without sharing data externally, a significant aspect for privacy-focused settings like healthcare.Federated finetuning permits each site to train a local model on its data and share only model updates, not the raw data, ensuring privacy.Implementing federated finetuning can optimize model performance in data-sensitive environments while upholding confidentiality.