Implementation of Federated Deep-Q to Improve Latency, Reliability, and Scalability in 6G URLLC Dynamic Resource Allocation

Ultra-Reliable Low Latency Communication (URLLC) is one of the key building blocks for 6G wireless networks which brings benefits to real-time services such as autonomous driving, industrial automation and remote surgery. Yet, the growing number of network devices and ever-stricter Quality of Service (QoS) demands increase in the latency, reliability and scale complexities. This research presents Federated Deep-Q (FedDQ) designed to combine Federated Learning (FL) with Deep Q-Learning to perform joint dynamic and distributed resource allocation in 6G URLLC systems. Unlike the conventional centralized reinforcement learning solutions which require frequent global communication and heavy computations, FedDQ enables edge devices to learn resource policies individually while sharing only the learned model parameters to a central server. It ensures data privacy, lower communication overhead, and scales well with increasing number of users. Deep-Q is made to capture intricate network states and adjust based on the user mobility, channel conditions and device density. The federated setting allows the model to better generalize across diverse local environments, augmenting not just reliability but also latency guarantees. Empirically the necessity of different ratios is demonstrated through extensive simulations in a 6G URLLC scenario with establishing varying latency budgets and packet drop constraints. FedDQ achieved better performances compared to baseline methods like centralized DQL and heuristic-based scheduling. The experiments verified a flicker free video streaming reduction of up to 28% in end-to-end latency, an increase in packet delivery reliability by as much as 34%, and spectral efficiency gains of over 22% under high density conditions. Scalability is tested with increasing device counts, so the federated setup should not slow down because there is little performance drop. This study proposes a fast adaptive and privacy-preserving resource allocation in the work to next-generation networks. The FedDQ model provides an inherent solution for this by remaining highly responsive and adaptable, but without compromising on URLLC-specific requirements. It is edge-friendly and scalable, well-suited for real-world 6G deployments including heterogenous and mobile devices.