The defence industry faces unprecedented challenges in adapting to evolving threats, and AI-driven solutions are pivotal in addressing these complexities with speed, precision, and scalability. Computer vision is revolutionizing industries by enabling machines to understand and interact with the world through visual data. However, deploying these systems in defense-related environments remains a challenge.
This talk explores how federated machine learning unlocks new possibilities, enabling decentralized, real-time adaptation of models in dynamic environments. Scaleout is part of a research and development initiative, “AI-based reconnaissance”, addressing the challenge of rapidly changing and unpredictable environments. The project leverages federated learning to enable real-time data collection, analysis, and model fine-tuning while ensuring that raw data remains localized and secure. The demonstration showcases the system’s ability to capture, annotate, and train models within a federated network, efficiently keeping models up-to-date in dynamic environments while operating under constrained network conditions.
David holds a Master’s degree in Engineering Physics, specializing in Scientific Computing and AI. He has done work in various fields, including cybersecurity, computer vision, and fintech. Now a Machine Learning Engineer at Scaleout Systems, David focuses on federated learning projects in the defence sector, tackling challenges in dynamic and secure environments.
