EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks

TL;DR

EdgeAgentX is a new framework combining federated learning, multi-agent reinforcement learning, and adversarial defenses to improve autonomous decision-making, latency, throughput, and robustness in military communication networks.

Contribution

It introduces EdgeAgentX, a comprehensive framework that integrates multiple AI techniques specifically designed for secure and efficient military communication networks.

Findings

Significantly improves decision-making and reduces latency.

Enhances throughput and robustness against adversarial attacks.

Validated through comprehensive simulations.

Abstract

This paper introduces EdgeAgentX, a novel framework integrating federated learning (FL), multi-agent reinforcement learning (MARL), and adversarial defense mechanisms, tailored for military communication networks. EdgeAgentX significantly improves autonomous decision-making, reduces latency, enhances throughput, and robustly withstands adversarial disruptions, as evidenced by comprehensive simulations.