AI at the Frontline: Transforming Defence in Unpredictable Environments

Overview

Join us in exploring the cutting-edge integration of federated machine learning in defense strategies. This course highlights how decentralized machine learning models adapt in real-time, even in unpredictable environments, while safeguarding data privacy and navigating network limitations.

Elevate your understanding of AI's transformative impact on defense operations through this engaging and insightful program available on YouTube.

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

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Subjects

Computer Science