<ul><li>Federated Learning is a decentralized approach to training machine learning models where the data stays on the user’s device or local server.</li><li>Model updates (not the data itself) are shared with a central server, ensuring privacy.</li><li>Federated Learning has practical applications in industries like healthcare, finance, and telecommunications.</li><li>While federated learning offers advantages, it also presents challenges in terms of efficiency and scalability.</li></ul>

Federated Learning: How AI Can Train Without Sharing Your Data

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