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

Unlocking the Power of Federated Finetuning

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