Documentation Index
Fetch the complete documentation index at: https://docs.elata.bio/llms.txt
Use this file to discover all available pages before exploring further.
Federated learning is a roadmap direction, not the default production integration path today.
What Federated Learning Means Here
In an Elata federated setup, app instances would train or adapt model weights locally, then send only constrained update artifacts for aggregation. The intended direction is:- process and train locally in the user environment
- send model updates instead of raw EEG or camera frames
- aggregate updates across many participants
- return improved shared model versions to clients
Why It Is On The Roadmap
Federated learning is a strong fit for biosignal products because it can improve model quality across device types and usage contexts while reducing privacy exposure. Planned benefits include:- better cross-user and cross-device robustness over time
- faster model iteration without requiring centralized raw-data collection
- clearer privacy posture for sensitive physiological signals
How It Preserves Privacy
Federated learning helps preserve privacy by changing what leaves the device:- raw biosignal inputs stay local by default
- shared artifacts are model updates, not full raw signal streams
- aggregation combines many updates before model rollout
Current Status
Federated learning is a roadmap direction, not the default production integration path today. For current integrations, use the existing package entrypoints:- EEG:
@elata-biosciences/eeg-weband@elata-biosciences/eeg-web-ble - rPPG:
@elata-biosciences/rppg-web
Next
EEG In A Browser
Browser EEG integration
rPPG In A Browser
Camera-based rPPG integration
Compatibility
Browser and device support