Federated Learning

What is Federated Learning?

 Federated Learning is a machine learning technique where multiple decentralized devices collaboratively train a shared model while keeping their data local and private. This approach is advantageous for applications requiring data privacy, such as healthcare and mobile devices.

How Federated Learning Works

 Federated Learning coordinates model training across distributed devices without centrally aggregating data. Devices compute model updates locally and share only encrypted gradients, preserving data privacy.

Federated Learning Benefits

  • Privacy Preservation: Protects sensitive data by keeping it local and minimizing the risk of data breaches during model training.
  • Scalability: Scales efficiently across numerous devices, enabling robust model training in diverse real-world environments.
  • Cost-Efficiency: Reduces bandwidth and computation costs associated with centralizing large datasets, making it feasible for edge devices with limited resources.

Use Cases for Federated Learning

  • Healthcare AI: Collaboratively train AI models across hospitals while preserving patient privacy.
  • Mobile Applications: Improve predictive text or voice recognition models on mobile devices without data sharing.
  • IoT Networks: Optimize energy consumption patterns in smart city applications while maintaining data privacy.
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