Aerospace & Defense
Enhance decision-making in critical environments with collaborative AI and guaranteed privacy
Accelerating aerospace and defense innovation—without compromising security
Sherpa.ai enables defense and aerospace organizations to collaborate on advanced AI capabilities—such as threat detection, predictive maintenance, and mission-critical decision support—without ever sharing sensitive operational data.
Through Sherpa.ai, agencies and partners can strengthen intelligence and resilience while maintaining full data sovereignty and compliance with security protocols.
The aerospace sector can unlock unprecedented potential through collaborative model training. From object detection to predictive maintenance, Sherpa.ai unlocks the potential of data from different sources to obtain more effective models for different situations.
Former Deputy Director, Office in Science and Technology Policy, The White House
Federated Learning Cases in Aerospace & Defense
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.
Predictive Maintenance and Logistics
Improve operational readiness of complex systems through federated analysis across units.
Scalable and Private Object Detection with Federated Learning
This study demonstrates how Sherpa.ai’s Federated Learning platform enables collaborative training of object detection models using YOLOv8—without ever sharing raw image data.
When dealing mission critical data centralized training is often infeasible due to security restrictions, bandwidth limitations, or the sensitivity of visual data. Sherpa.ai addresses these challenges by applying Horizontal Federated Learning (HFL) to train YOLOv8 models across multiple vehicles or organizations, ensuring data privacy and minimizing communication overhead.
This case study illustrates how Sherpa.ai’s platform meets the technical, security, and operational needs of real-time, distributed AI in edge situations —enabling scalable, privacy-preserving object detection without sacrificing performance.
Aerospace Predictive Maintenance with Privacy and Energy Efficiency
This project demonstrates the value of Sherpa.ai to improve prediction of aircraft engine failures —without transferring any raw sensor data across systems.
In certain safety-critical environments like aerospace, data centralization is often infeasible due to regulatory, privacy, and communication limitations. Sherpa.ai addresses these challenges by enabling collaborative AI model training directly at the edge—keeping data on-device and only exchanging encrypted model parameters.
Results show that the federated models can match the performance of centralized training while significantly outperforming isolated, single-node models. Moreover, the solution reduces data transfer overhead, protects proprietary data, and improves energy efficiency—critical for real-time, resource-constrained environments like satellites, aircrafts, or remote fleets.
This case study demonstrate how Sherpa.ai’s platform meets the technical, regulatory, and operational demands of predictive maintenance in Industry 4.0 and aerospace scenarios—delivering accurate, scalable, and privacy-preserving AI at the edge
