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

2025/05/24 by Ray, Abir
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2505.18457

openalex publication_date 2025/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Adversarial Examples Are Not Bugs, They Are Features
• Towards Federated Learning at Scale: System Design
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
• Towards Evaluating the Robustness of Neural Networks
• Federated Optimization:Distributed Optimization Beyond the Datacenter

Related

• EdgeAgentX-DT: Integrating Digital Twins and Generative AI for Resilient Edge Intelligence in Tactical Networks
• AI-Driven Tactical Communications and Networking for Defense: A Survey and Emerging Trends
• Agent Communications toward Agentic AI at Edge -- A Case Study of the Agent2Agent Protocol
• CAGE challenge 4: A scalable multi‐agent reinforcement learning gym for autonomous cyber defence
• Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey