TensorFlow Federated

TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. It enables developers to train machine learning models across multiple clients (e.g., mobile devices or edge servers) without centralizing the data, using federated learning algorithms. This approach enhances privacy and reduces data transfer by keeping raw data on local devices while aggregating model updates.

Also known as: TFF, Tensorflow Federated, TensorFlow Federated Learning, Federated TensorFlow, TF Federated

Why learn TensorFlow Federated?

Developers should learn TensorFlow Federated when building privacy-preserving machine learning applications, such as on-device training for smartphones, healthcare data analysis without sharing sensitive information, or collaborative learning across distributed IoT devices. It is particularly useful in scenarios where data cannot be centralized due to regulatory constraints (e.g., GDPR) or bandwidth limitations, allowing for scalable and secure model training.

Learning Resources

• docs: TensorFlow Federated Documentation
• tutorial: Federated Learning with TensorFlow Federated Tutorial
• video: TensorFlow Federated: Machine Learning on Decentralized Data (Google Developers Video)
• book: Hands-On Federated Learning with TensorFlow Federated (Book)