# Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G

arXiv: https://arxiv.org/abs/2603.09141 · HTML: https://mlsanity.com/paper/2603.09141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Federated Learning-Empowered AI-Generated Content in Wireless Networks](https://mlsanity.com/paper/2307.07146/) (2023)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN](https://mlsanity.com/paper/2510.05255/) (2025)
- [A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks](https://mlsanity.com/paper/2510.19973/) (2025)
- [SemSpaceFL: A Collaborative Hierarchical Federated Learning Framework for Semantic Communication in 6G LEO Satellites](https://mlsanity.com/paper/2505.00966/) (2025)
- [Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers](https://mlsanity.com/paper/2511.22616/) (2025)

## Built on by (most cited first, 1 of 1)

- [6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence](https://mlsanity.com/paper/2605.01546/) (2026)

## Abstract (arXiv)

The shift toward user-customized on-device learning places new demands on wireless systems: models must be trained on diverse, distributed data while meeting strict latency, bandwidth, and reliability constraints. To address this, we propose an Agentic AI as the control layer for managing federated learning (FL) over 6G networks, which translates high-level task goals into actions that are aware of network conditions. Rather than simply viewing FL as a learning challenge, our system sees it as a combined task of learning and network management. A set of specialized agents focused on retrieval, planning, coding, and evaluation utilizes monitoring tools and optimization methods to handle client selection, incentive structuring, scheduling, resource allocation, adaptive local training, and code generation. The use of closed-loop evaluation and memory allows the system to consistently refine its decisions, taking into account varying signal-to-noise ratios, bandwidth conditions, and device capabilities. Finally, our case study has demonstrated the effectiveness of the Agentic AI system's use of tools for achieving high performance.