<ul><li>Federated Learning (FL) is an approach to machine learning that keeps user data on devices, contributing to a shared global model without centralized data collection.</li><li>FL addresses data privacy concerns by ensuring raw data remains on users' devices, supporting privacy regulations like GDPR, HIPAA, and India's DPDP Act.</li><li>The training process in FL involves multiple rounds where only model updates are transmitted, reducing network loads compared to raw data transfers.</li><li>FL enables personalized models based on local user behavior while still contributing to a generalized model, allowing real-time learning at the edge with intermittent connectivity.</li></ul>

What is Federated Learning?

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