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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.18457 |
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| _version_ | 1866908378590085120 |
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| author | Ray, Abir |
| author_facet | Ray, Abir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks Ray, Abir Artificial Intelligence Machine Learning Multiagent Systems 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. |
| title | EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks |
| topic | Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2505.18457 |