Federated learning

Federated learning can significantly enhance data quality and model accuracy by:

• Federated learning allows models to be trained directly where the data is generated.
• By processing data locally, federated learning minimizes the need to transmit sensitive or large volumes of data.
• The ability to train models on data from multiple sources directly on the edge helps overcome challenges associated with data scarcity.

Federated learning offers a promising solution by optimizing data processing and AI model training directly at the source of data collection.

AI Challenge

Accessing data for developing AI models faces several challenges in the military context:

• Fragmented and inaccessible data across different systems
• Security and privacy concerns complicate data sharing

Federated learning

Federated learning can improve data quality and model accuracy because:

• Models can learn from the diverse and distributed data sets
• Sensitive data can be processed locally, reducing the risk of compromising operational security
• By training on data from multiple sources, federated learning can mitigate the issue of data scarcity

AI Challenge

Involves analyzing heterogeneous and high-volume data streams. Challenging because:

• Of the complexity and variability of network traffic
• Difficult to keep confidentiality and integrity of sensitive information while using it to train AI models

Federated learning

Federated learning can enhance intrusion detection by:

• Learning from diverse data sources across various network segments without centralizing sensitive data
• Processing data locally, which is crucial for adhering to privacy regulations and safeguarding sensitive information