Distributed AI
Abstract
Effectively leveraging modern artificial intelligence (AI) and machine learning (ML) techniques for both enterprise and tactical applications requires robust distributed AI capabilities. This research improves these capabilities with a focus on quickly and efficiently training and deploying models across enterprise and tactical systems, federated learning implementations, deploying state-of-the-art AI and ML algorithms onto ruggedized edge hardware and small form-factor devices with computing capabilities, improving robotic autonomous systems and models deployed on robotic platforms, and governing a large portfolio of distributed ML models. As the distributed network of data and AI/ML models grows and becomes more integrated into warfighting functions, it becomes a bigger attack vector for adversaries. In order to keep ongoing AI and ML developments secure, this research also investigates techniques to attack and compromise AI and ML systems as well as to defend them from attacks.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2025
- Source ID
- 6cf2ce1be20f4fcecaf1ce8c69837577