Design of an Iterative Method for Collaborative Resource Management using MAPPO and Federated Learning in 5G Networks

Abstract

With the increasing real-world deployment of 5G networks and proliferation of heterogeneous IoT devices, an efficient resource management becomes inescapable to handle dynamic and diverse traffic demands. In conventional ways, resource allocation and model training in 5G networks become cumbersome due to scalability, heterogeneity, and cross-domain adaptability. They cannot capture the collaboration among base stations, personalization of IoT devices, and variational urban and rural network scenes. To overcome these limitations, in this work a novel amalgamation of Multi-Agent Proximal Policy Optimization, Task-Aware Personalized Federated Learning, and Domain-Adaptive Transfer Learning is proposed for the collaborative resource management, heterogeneous task learning, and cross-domain optimization in 5G and IoT networks. Preliminary, that is, the extension of Proximal Policy Optimization to multi-agent settings enables multiple base stations to cooperate on finding the optimal strategy of bounded latency, high-throughput, and network congestion. TA-PFed updates models for task-specific training among heterogeneous IoT devices such as sensors and cameras to become specialized in their respective tasks while leveraging the shared knowledge from the global models. It reduces the communication cost and enhances privacy by limiting data sharing. DATL thus allows the knowledge transfer across network environments, hence fastening resource management optimization in new domains (e.g., rural networks) without extensive retraining. These all enable addressing three important challenges: collaborative resource allocation, personalized learning for diverse IoT tasks, and fast adaptation across domains, each addressed in 5G networks using the proposed fusion approach. Numerical results indicate enormous gains: 45% gain in endtoend network throughput, 30-35% reduction in latency, 20-25% reduction in packet loss, and 90% reduction in privacy risk. Besides that, there is a 50% reduction in cross-domain adaptation time and 40% reduction in the data transmission cost. It lays the foundation for future 5G and IoT deployments, which scale with diversified and dynamic network environments efficiently while preserving data privacy.