AI at the Frontline: Transforming Defence in Unpredictable Environments

Overview

Explore how federated machine learning transforms defense operations by enabling decentralized, real-time model adaptation in dynamic environments while maintaining data security and operating under network constraints.

Syllabus

• Introduction to AI and Defense
Overview of AI in modern defense operations
Key challenges in defense environments
• Understanding Federated Machine Learning (FML)
FML fundamentals and architecture
Comparison with centralized machine learning
• Data Security in FML
Privacy-preserving techniques
Secure multi-party computation in FML
• Real-time Model Adaptation
Techniques for real-time learning and adaptation
Handling dynamic and unpredictable environments
• Decentralized Decision Making
Coordinating and integrating distributed models
Case studies of decentralized models in defense settings
• Operating Under Network Constraints
Strategies for minimizing communication
Prioritization and synchronization of model updates
• Applications of FML in Defense
Threat detection and situational awareness
Autonomous systems and tactical decision support
• Challenges and Considerations
Ethical implications and bias in defense AI
Technical limitations and future directions
• Capstone Project
Real-world scenarios and implementation of FML in defense operations
Team presentations and peer feedback
• Conclusion and Future Trends
Emerging trends in AI for defense
The future of federated learning in military applications

Subjects

Computer Science