Sustainability & AI
Federated learning
Training AI models across distributed devices without centralising raw data, relevant to data sovereignty and privacy.
Definition
A machine-learning approach in which a shared model is trained across many devices or organisations without centralising their raw data.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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Overview
What it means
Introduced by Google researchers in 2017, federated learning sends model updates — not data — from participants to a coordinating server, which aggregates them. Privacy is improved by design, though updates can still leak information without additional safeguards.
How it is used
Proposed sustainability uses include training across farms, utilities or factories that cannot pool raw operational data, and mobility or energy models built from devices that keep personal data local.
Why it matters
Data about emissions, yields and consumption is often commercially or legally sensitive. Federated learning offers a route to shared models without shared data — relevant wherever confidentiality blocks collaborative sustainability analytics.
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