Train a model without seeing the data.
Train one model across several organizations, each keeping its own data. It handles privacy, unreliable or dishonest sites, and sites that drop offline.
Cross-organization deployment
Round 1,412
1,412
Round
6 / 6
Participants reporting
2.4 / 8.0
ε privacy budget spent
0.024
Model drift across sites
What the aggregator cannot see.
Secure aggregation
Only the combined result is visible
The aggregator sees only the sum of all participants’ updates. Each site’s individual contribution is cryptographically hidden.
Differential privacy
A measurable privacy budget per site
Differential privacy is a mathematical guarantee on how much any single data record can influence the trained model. Each site has a budget (denoted ε); training stops before it’s exhausted.
Resilient to bad actors
Robust if up to a third of sites misbehave
Training still converges even when some participants send corrupted or malicious updates. Several standard robust aggregation methods are available.
Offline-tolerant
Sites can drop out or run slow
The aggregator handles late or missing updates. One site going down does not stall the training.