Edge-centric energy efficient LSTM with federated learning model based computation offloading for IoT applications

Abstract

Abstract A more recent innovation to support cloud computing is edge computing that can address the deficiency of the existing centralised cloud computing paradigm and bring compute and storage resources closer to devices. Edge computing isn’t always the same as conventional cloud computing. Edge computing isn’t always the same as conventional cloud computing. It is a new computational model that performs computation at the network edge. Its crucial idea is to bring computing in the way of the information’s source. Task scheduling is the process allocating incoming requests (tasks) using a specific method to make the best use of the resources for the needed process. Users of services must submit their requests online because cloud computing is the technology used to deliver services through the internet. This paper aims to provide an Edge centric LSTM model with Federated learning for Data storage and model training that will take place on powerful edge servers in distributed ML methods. a massively distributed tasks requiring Processing on-site, coordinating the task remotely and execution are carried out in collaboration with number of multiple edge nodes and the distant cloud infrastructure. As a result, improved computation offloading and networking tradeoffs are possible to achieve low latency, large bandwidth take place on powerful edge servers.