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Federated Learning 📱

Practical AI

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Federated Learning: Privacy and Practicality

This chapter explores the nuances of federated learning, emphasizing its applications in devices like cars and phones while addressing concerns about battery consumption and data privacy. It discusses the importance of frameworks such as TensorFlow Federated and the challenges of navigating national regulations. Additionally, the chapter highlights real-world use cases, including healthcare applications, that demonstrate the balance between privacy and advanced functionality.

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