Federated Learning

Decentralized training across devices with privacy protection.

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Federated learning trains locally on user devices or sites and aggregates only model updates centrally.

• Advantages: Better data privacy and ownership, reduced raw data transfer.
• Add-ons: Secure aggregation, differential privacy, management of device heterogeneity.
• Use cases: Mobile devices, healthcare, industrial IoT.