methodology

Federated Learning

Federated Learning is a machine learning approach that enables model training across decentralized devices or servers holding local data samples, without exchanging the data itself. It allows multiple parties to collaboratively train a shared model while keeping their data private and on-premises, addressing privacy and data governance concerns. This technique is particularly useful in scenarios where data cannot be centralized due to regulatory, security, or logistical constraints.

Also known as: FL, Federated ML, Distributed Learning, Collaborative Learning, Privacy-Preserving ML
🧊Why learn Federated Learning?

Developers should learn Federated Learning when building applications that require privacy-preserving machine learning, such as in healthcare, finance, or mobile devices where user data cannot be shared. It's essential for use cases like training predictive models on sensitive data from multiple hospitals, improving keyboard suggestions on smartphones without uploading typing data, or enabling cross-organizational AI collaborations while complying with GDPR or HIPAA regulations.

Compare Federated Learning

Federated Learning vs Centralized Machine Learning→Federated Learning vs Edge Computing→Federated Learning vs Homomorphic Encryption→

Learning Resources

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Federated Learning: Collaborative Machine Learning without Centralized Training Data
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Federated Learning Tutorial - TensorFlow
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Federated Learning: Strategies for Improving Communication Efficiency
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Federated Learning Course on Coursera
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Introduction to Federated Learning - YouTube
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Related Tools

Machine LearningPrivacy Preserving TechniquesDistributed SystemsDifferential PrivacyModel Aggregation

Alternatives to Federated Learning

Centralized Machine LearningEdge ComputingHomomorphic Encryption