Decrease of Communication Workloads Over Green Scheduling Algorithm Based Federated Learning with Erlang in Big Data Using IoT

F-SLA(Federated service-level agreement) is a promise towards a service benefactor and a customer in which the prior undertakes to supply a service to the final under certain conditions. Time or performance are examples of specialized phrases. To abide by the F-SLA benefactor must keep the focus on the situation. Cloud processing performance should be regularly monitored. F-SLA-related research and determination-associated concerns provide a significant barrier. GS (Green Scheduling) was created to reduce power usage as much as feasible. The primary goal of this work is to progress the methods of resource scheduling to increase the power proficiency of data sectors by turning them off and leaving them idle. Federated learning (FL) recently commences as a capable concept to quarter these concerns, by resources of learning a universal model by gathering local apprises from various devices without allotment the privacy-complex data. The ever-growing quantity of Internet of Things (IoT) devices are endlessly producing enormous masses of data, but the at present cloud-centric method for IoT big data analysis has raised public concerns on both data confidentiality and network cost. In the meantime, GS aims to provide a negotiated F-SLA as well as power-aware solutions, setting aside other concerns.