Federated Learning

Federated Learning enables organizations to train massive AI models collaboratively without centralizing sensitive data. Instead of moving data to the model, the model is sent to the data. Local devices compute updates to the model based on their isolated datasets, and only these mathematical updates (not the raw data) are aggregated centrally. This strictly preserves Data Privacy and is highly favored in healthcare and defense deployments.