mlsanityData to 14 Oct 2026

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

arXiv 2505.18457 ↗First posted 24 May 2025cs.AI cs.LG cs.MAcited by 0

On the map: LLM agents and tool use → Agentic AI for wireless networks

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 6 of its 8 references that are in the map. First posted on arXiv 24 May 2025.

Builds on

6 of 8 references.

References · 6

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  4. 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 953
  5. 2019Towards Federated Learning at Scale: System Designcited by 832
  6. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 195

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