Aerospace & Defense
Enhance decision-making in critical environments with collaborative AI and guaranteed privacy
The application of Artificial Intelligence through Sherpa.ai’s Privacy-Preserving platform will allow the prediction algorithm to improve the diagnosis without the need to share any patient data. This platform may enable testing of diagnosis and therapeutics for a group of diseases that are currently without specific treatment options.
Former Deputy Director, Office in Science and Technology Policy, The White House
Use Cases
Intelligence Fusion Across Agencies
Enable different defense branches to share analytical capabilities without revealing classified information.
AI-Powered Tactical Scenario Simulation
Train predictive models with distributed data to anticipate high-risk situations.
Real-Time Threat Recognition
Combine data from multiple sources to detect threats in the field without compromising security.
Efficient Military Maintenance and Logistics
Improve operational readiness of complex systems through federated analysis across units.
Scalable and Private Object Detection with Federated Learning
This study evaluates training object detection models (YOLOv8) in privacy-sensitive environments without sharing data or metadata. Sherpa.ai’s Horizontal Federated Learning significantly outperforms local baselines, proving its effectiveness while ensuring full data privacy.
Aerospace Predictive Maintenance with Privacy and Energy Efficiency
This study demonstrates how Sherpa.ai applies Federated Learning to predict aircraft engine failures without transferring sensitive data. Using a NASA turbine dataset, the approach enhances predictive accuracy while minimizing energy consumption and bandwidth usage—meeting the demanding requirements of the aerospace sector.
