Data Science and Machine Learning Applications

Federated Learning

Federated Learning is a decentralized approach to machine learning where multiple devices collaboratively train a model without sharing their local data. Instead of sending raw data to a central server, each device trains the model locally and only shares updates (like model weights) with the central server. This method enhances privacy and security while leveraging distributed data sources, making it ideal for applications involving sensitive information or where data is distributed across many locations.