How can deep learning improve resilient multi-agent decisions?
Deep Learning for Resilient Adversarial Decision Fusion in Byzantine Networks
This paper proposes a deep learning approach to improve the reliability of decision-making in multi-sensor networks where some sensors (Byzantine nodes) might be malicious and send false data. It uses a neural network to learn patterns from sensor reports and predict the true system state, even when a significant portion of the sensors are compromised.
Key points for LLM-based multi-agent systems: The approach offers a robust method to aggregate information from multiple LLMs, even if some LLMs are behaving erratically or adversarially. The global dataset training strategy could be adapted to train a central agent to interpret and combine outputs from a diverse set of LLMs without needing prior knowledge of their individual characteristics or potential biases. The focus on scalability is relevant to managing large numbers of LLMs in a multi-agent application.