Platform Overview

Meditopia

Secure federated fine-tuning — enabling model training on sensitive health data without data ever leaving the premises.

Meditopia Secure Fine-Tuning Flow Health Org 1 shares encrypted data, sample data and instructions into Meditopia's secure enclave. Health Org 2 submits a base model. Fine-tuning completes inside the enclave. Only the trained model weights leave. All data is purged. Health Org 1 On-prem server Patient records & data Data warehouse Structured datasets 📋 Instructions Usage rules & scope 🗂 Sample data Context for fine-tuning 🔒 on-prem Data owner Meditopia 🔐 Secure enclave Fine-tuning No raw data exposed Encrypted access only Isolated compute 🛡 zero data residue Health Org 2 Base model Uploads for fine-tuning Never sees raw data from Health Org 1 Fine-tuned model Returned to Org 2 Model requester encrypted base model tuned weights
Step 01
Encrypted data in
Org 1 shares data, usage instructions, and sample data via secure channel — all encrypted in transit and at rest.
Step 02
Isolated fine-tuning
Model trains inside Meditopia's enclave using the provided data and context. No raw data is ever exposed.
Step 03
Model returned
Only trained weights exit the enclave. Org 2 never touches raw data.
Step 04
Full data purge
All temporary data deleted. Zero trace on-cloud or off-premise.

🛡  No data leaves the originating premises  ·  No cloud residue after completion