InFeMo: Flexible Big Data management through a federated Cloud system

Abstract

This paper introduces and describes a novel architecture scenario based on Cloud Computing and count on the innovative model of Federated Learning. The proposed model named Integrated Federated Model, with acronym InFeMo. InFeMo incorporates all the existing Cloud models with a federated learning scenario, as well as other related technologies that may have integrated use with each other, offering a novel integrated scenario. In addition to this, proposed model is motivated to deliver a more energy efficient system architecture and environment for the users, which aims to the scope of data management. Also, by applying the InFeMo the user would have less waiting time in every procedure queue. Proposed system was built on the resources made available by Cloud Service Providers (CSPs), by using the PaaS (Platform as a Service) model, in order to be able to handle user requests better and faster. This research tries to fill a scientific gap in the field of federated Cloud systems. Thus, taking advantage of the existing scenarios of FedAvg and CO-OP, we keen to ended up to a new federated scenario that merges these two algorithms, and aiming to has a more efficient model, that it is able to select, depending on the occasion, if it “train” the model locally in client of globally in server.