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This research introduces a revolutionary paradigm for HetNet management,presenting an innovative algorithmic framework that transcends traditionalnotions of network capacity enhancement. Our exploration delves into theintricate interplay among distinct components, weaving together metaheuristicalgorithms, Neural Networks optimization, and Federated Learning approaches.The primary focus is on optimizing capacity in IoT-based heterogeneous networkswhile ensuring impeccable coverage and data reliability. Employing multi-layeroptimization methods, we propose a dynamic model for optimal transmissionstrategy, strategically allocating replicas within cloud computing environmentsto curtail data access costs. Our algorithm not only discerns optimal datareplication locations but also navigates the delicate balance between spectralefficiency and ergodic capacity in cellular IoT networks with small cells usingon/off control. The orchestrated interplay between metaheuristic algorithms,Neural Networks optimization, and Federated Learning orchestrates resourcereallocation, attaining an optimal balance between spectral efficiency, powerutility, and ergodic capacity based on Quality of Service (QoS) requirements.Simulation results corroborate the efficacy of our approach, showcasingenhanced tradeoffs between spectral efficiency and total ergodic capacity withdiminished outage probability compared to prevailing algorithms across diversescenarios.

Hierarchical Optimization of Metaheuristic Algorithms and Federated Learning for Enhanced Capacity Management and Load Balancing in HetNets