Federated Learning

Federated learning module provides a machine learning approach that enables collaborative training of models without directly sharing underlying data

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Benefits

Develop superior models faster, especially industry-specific models requiring input from multiple industry organisations, with data privacy throughout the process

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Features

Distributed Training

Data remains on individual devices or servers (called clients) where local models are trained.

Model Updates

Clients only share model updates (changes in weights and biases) with a central server (coordinator). These updates contain no raw data.

Aggregation

The coordinator aggregates the received updates to improve a global model.

Privacy-Preserving Techniques

Techniques like differential privacy can be used to add noise to the updates, further protecting individual contributions.

Unified AI Governance and Advanced Data Privacy