Industry 4.0
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
Collaborative Predictive Maintenance
Optimize operations across production plants without sharing sensitive internal information.
Supply Chain Optimization
Improve efficiency across the entire logistics network while preserving each participant’s data privacy.
AI-Driven Product Quality
Detect production defects using AI models trained on distributed data with no risk of IP leakage.
Collaborative Development Among Manufacturers
Accelerate innovation through joint learning without disclosing proprietary data.
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.
Scalable and Private Anomaly 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.
