Federated Learning Lab
Train across siloed data without moving it. Privacy-preserving aggregation and audit trails.
/ Overview
Federated Learning Lab will let you train models across siloed data without moving it. Each participant trains locally; we aggregate updates with privacy-preserving techniques and full audit trails. Planned for late 2026.
/ The_Problem
Data stays locked in hospitals, banks, and enterprises. Federated learning trains across silos without moving raw data—but building secure aggregation and audit trails is hard. We're building the protocol and platform so you can federate training without a PhD in crypto.
/ Privacy-preserving_aggregation
Participants train locally and send only encrypted or noised updates. We aggregate and broadcast a new global model. No raw data ever leaves the source; full audit trails for compliance.
“Federated Learning Lab is our bet on the next wave of enterprise ML: train where the data lives, without the integration nightmare.”
/ Roadmap
Protocol design
Aggregation, security model
Pilot partners
2–3 orgs, limited data types