Explanation-Guided Federated Deep Reinforcement Learning for Joint Resource Allocation and Scheduling in 6G in-X Subnetworks
This paper addresses the challenges of dynamic resource allocation and scheduling in 6G in-X subnetworks supporting applications with heterogeneous characteristics by proposing a novel framework that combines Multi-Agent Reinforcement Learning (MARL), Federated Learning (FL), and Explainable AI (XAI).