Federated Learning
A machine learning approach where models are trained across multiple decentralised devices or servers holding local data, without exchanging raw data, thereby preserving data privacy.
In plain language
Training AI across many devices without moving the data. Your phone can help improve a keyboard prediction AI using your typing patterns, but your actual messages never leave your phone.
Why this matters
Federated learning supports data sovereignty and privacy compliance by keeping data where it resides. For organisations operating across jurisdictions or handling sensitive data, it is a strategic implementation approach that enables AI development without centralising sensitive information.
Relevance
StrategyFederated learning reduces regulatory and reputational risk in multi-jurisdiction environments by enabling AI capability without data centralisation.
Related terms
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