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The constant surge in the traffic demand on cellular networks has led tocontinuous expansion in network capacity in order to accommodate existing andnew service demands. This has given rise to ultra-dense base station deploymentin 5G and beyond networks which leads to increased energy consumption in thenetwork. Hence, these ultra-dense base station deployments must be operated ina way that the energy consumption of the network can be adapted to thespatio-temporal traffic demands on the network in order to minimize the overallenergy consumption of the network. To achieve this goal, we leverage twoartificial intelligence algorithms, federated learning and actor-criticalgorithm, to develop a proactive and intelligent base station switchingframework that can learn the operating policy of the small base station in anultra-dense heterogeneous network (UDHN) that would result in maximum energysaving in the network while respecting the quality of service (QoS)constraints. The performance evaluation reveals that the proposed framework canachieve an energy saving that is about 77% more than that of thestate-of-the-art solutions while respecting the QoS constraints of the network.

FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks