Federated Learning
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May 1, 2024
3 minute read
Federated Learning is a distributed machine learning technique that enables multiple devices or entities to train a shared machine learning model without sharing their data. Each device trains a local model using its own data and then shares the model updates with a central server. The central server aggregates the updates and uses them to train a global model, which is then distributed back to the devices. This process is repeated until the global model converges.
Benefits of Federated Learning
Federated Learning offers several benefits over traditional machine learning approaches:
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Find a path to becoming a Federated Learning. Learn more at:
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