What is Federated Learning?

It is a symmetric-key algorithm for encrypting digital data. Developed in the 1970s, it was influential in cryptography.

How it Works?

Local training: Models are rained on individual devices.

Model updates: Updates are shared with a central server.

Global model: A global model is created from the updates.

Improved privacy

Enhanced security

Reduced data transfer

Increased efficiency

Benefits of Federated Learning

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Healthcare

IoT

Finance

Mobile apps

Use Cases

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Data heterogeneity

Communication overhead

Model convergence

Challenges and Solutions

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Advancements in privacy-preserving techniques and distributed computing will drive its adoption.

The Future of Federated Learning

Revolutionize your AI applications while protecting data privacy.

Embrace the Power of Federated Learning