  This research introduces a revolutionary paradigm for HetNet management,
presenting an innovative algorithmic framework that transcends traditional
notions of network capacity enhancement. Our exploration delves into the
intricate interplay among distinct components, weaving together metaheuristic
algorithms, Neural Networks optimization, and Federated Learning approaches.
The primary focus is on optimizing capacity in IoT-based heterogeneous networks
while ensuring impeccable coverage and data reliability. Employing multi-layer
optimization methods, we propose a dynamic model for optimal transmission
strategy, strategically allocating replicas within cloud computing environments
to curtail data access costs. Our algorithm not only discerns optimal data
replication locations but also navigates the delicate balance between spectral
efficiency and ergodic capacity in cellular IoT networks with small cells using
on/off control. The orchestrated interplay between metaheuristic algorithms,
Neural Networks optimization, and Federated Learning orchestrates resource
reallocation, attaining an optimal balance between spectral efficiency, power
utility, and ergodic capacity based on Quality of Service (QoS) requirements.
Simulation results corroborate the efficacy of our approach, showcasing
enhanced tradeoffs between spectral efficiency and total ergodic capacity with
diminished outage probability compared to prevailing algorithms across diverse
scenarios.
