Federated Learning: Privacy-Preserving Distributed ML
Design federated learning systems that train models across decentralized data sources without centralizing sensitive information.
Federated learning allows you to train machine learning models on data that remains on edge devices or private servers rather than aggregating it in a central location. Participants keep raw data local and instead send model updates (gradients) to a central server, which aggregates them to improve a shared model. This approach preserves privacy because sensitive data never leaves its source, addressing regulatory requirements like GDPR and HIPAA. Federated learning is essential for healthcare, finance, and mobile applications where data cannot be centralized. Challenges include communication overhead (sending large gradient tensors), non-IID (non-independent and identically distributed) data across participants, and…
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