Federated computing (or federated learning/analytics) is an architecture where data stays within the local institution while models or queries travel across participating nodes. Only aggregated insights, parameters, or statistics are shared centrally — not raw personal data.
This approach is particularly powerful for organizations that want to generate Real World Evidence (RWE) while maintaining LGPD compliance.
Federated computing (or federated learning/analytics) is an architecture where data stays within the local institution while models or queries travel across participating nodes. Only aggregated insights, parameters, or statistics are shared centrally — not raw personal data.
This approach is particularly powerful for organizations that want to generate Real World Evidence (RWE) while maintaining LGPD compliance.
The LGPD emphasizes:
Traditional centralized data pooling can increase:
Federated computing directly mitigates many of these issues.
RWE relies on large, diverse datasets from real clinical practice.
Federated systems allow:
✔ Multi-site collaboration
✔ Larger effective sample sizes
✔ Inclusion of sensitive datasets (e.g., EHR, imaging, genomics)
✔ Faster study execution without data migration
