Federated Analytics
Techniques for computing analytics and insights across distributed datasets without moving or centralizing the raw data. Each participant computes locally and only shares aggregated results.
Why It Matters
Federated analytics enables cross-organization insights while preserving data privacy. It is especially valuable in healthcare, finance, and government where data sharing is restricted.
Example
Ten hospitals computing average treatment outcomes across their combined patient populations without any hospital sharing individual patient records.
Think of it like...
Like a census where each household reports summary statistics but never shares personal details — the aggregate picture is accurate without compromising individual privacy.
Related Terms
Federated Learning
A decentralized training approach where a model is trained across multiple devices or organizations without sharing raw data. Each participant trains locally and only shares model updates.
Differential Privacy
A mathematical framework that provides provable privacy guarantees when analyzing or learning from data. It ensures that the output of any analysis is approximately the same whether or not any individual's data is included.
Data Privacy
The right of individuals to control how their personal information is collected, used, stored, and shared. In AI, data privacy concerns arise from training data, user interactions, and model outputs.