Distributed Raman Spectrum Data Augmentation System Using Federated Learning with Deep Generative Models.

Area of Science:

• Chemical warfare defense
• Spectroscopy
• Artificial Intelligence

Background:

• Raman spectroscopy detectors face limitations in accurately identifying chemical agents on battlefields due to environmental variability and the challenge of acquiring diverse training data.
• Existing rule-based detection systems struggle with flexibility and reactivity when encountering new chemical substances.

Purpose of the Study:

• To develop a distributed Raman spectrum data augmentation system for enhancing artificial intelligence (AI)-based chemical agent detection.
• To overcome the data acquisition challenges associated with hazardous chemical agents by leveraging federated learning (FL) and deep generative models.

Main Methods:

• The proposed system integrates federated learning (FL) with deep generative models, including generative adversarial networks (GANs) and autoencoders.
• It employs various techniques to generate diverse and realistic Raman spectrum data, enabling collaborative model training among decentralized units without raw data exchange.

Main Results:

• The system demonstrated faster model training through decentralized cooperation.
• Generated Raman spectrum data exhibited high realism and diversity.
• The AI-based classification model showed significantly improved learning speed and performance compared to existing systems.

Conclusions:

• The federated learning and generative model approach effectively addresses data scarcity and variability in chemical agent detection.
• The developed system enables rapid and accurate AI-driven detection of chemical agents, enhancing soldier safety on the battlefield.