Federated Learning
Date of Addition
22 March 2025
A decentralised machine learning approach where multiple organizations or devices train models collaboratively without sharing raw data. Instead, only model updates or parameters are exchanged, ensuring data privacy while leveraging distributed data for improved model accuracy.
Federated learning involves a two-step process: during training, local models are trained on local datasets with only parameters (not raw data) exchanged between participants to build a global model; during inference, the model is stored on the user device for quick predictions. This approach offers several advantages: privacy-preserving AI development, personalised and adaptive models, lower bandwidth usage, and improved security through decentralisation.
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