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

2026-03-09

AI Summary

This work addresses the challenges of low latency, bandwidth constraints, and high reliability in user-customized on-device federated learning for 6G networks by formulating federated learning as a joint learning-and-network optimization problem. It introduces, for the first time, an Agentic AI endowed with retrieval, planning, encoding, and evaluation capabilities as the control plane, establishing a multi-agent-driven dynamic decision-making framework that jointly optimizes client selection, incentive mechanisms, resource allocation, and adaptive training. The system dynamically maps task objectives to network-aware actions based on real-time network conditions—such as signal-to-noise ratio, bandwidth, and device capabilities—and continuously refines its strategies through closed-loop evaluation and memory mechanisms. Experimental results demonstrate that the proposed approach significantly enhances the performance and efficiency of federated learning in complex 6G environments.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Federated Learning
• 6G networks
• Network control
• On-device learning
• Distributed data

Innovation

• Agentic AI
• Federated Learning
• 6G networks
• Network Control Plane
• Closed-loop Optimization