In the modern cybersecurity and defence landscape, strong machine learning models directly rely on access to data. Federated learning is a key technology to solve the data challenge where data cannot be shared between parties or moved off site.
In intelligence gathering, surveillance, and reconnaissance, large amounts of data is gathered through sensors.
Edge processing poses significant challenges, in some cases due to the limited bandwidth and in some cases due to the amount of data generated.
Federated learning can significantly enhance data quality and model accuracy by:
Federated learning offers a promising solution by optimizing data processing and AI model training directly at the source of data collection.
Using AI for predictive maintenance and failure prediction to help forecast failures in vehicles and machinery.
Accessing data for developing AI models faces several challenges in the military context:
Federated learning can improve data quality and model accuracy because:
AI models can learn to recognize complex patterns and anomalies that may indicate a cyber intrusion.
Involves analyzing heterogeneous and high-volume data streams. Challenging because:
Federated learning can enhance intrusion detection by: