Abstract: Systems and methods for noise agnostic federated learning are disclosed. A method may include a client computer program executed by an electronic device in a federated learning computer network comprising a plurality of clients: receiving, from a federated learning computer program, a data format having desirable noise characteristics; transforming a client data set comprising variable noise characteristics to the data format using a client generative adversarial network (GAN); generating client weights for the transformed client data set, wherein the client weights indicate features of the client data set; communicating the client weights to the federated learning computer program; receiving, from the federated learning computer program, adjusted weights, wherein the adjusted weights are based on the client weights and a plurality client weights received from the clients in the federated learning computer network; and updating the client weights for a client machine learning model using the adjusted weights.

Abstract: A framework for implementing Business Intelligence (BI) solutions at an organization level is provided. The framework includes an architecture module, a process module, an operations and support module, a governance module and a delivery module. The architecture module includes an execution architecture, an operations architecture and a development architecture. The process module manages processes implemented by Business Units (BUs) to provide BI solutions. The operations and support module manages the infrastructural requirements of the framework. The governance module manages the organizational requirements related to the implementation of the framework. The delivery module manages the delivery of the processed business data to the BUs. The process module, the operations and support module, the governance module and the delivery module are implemented, based on the architecture module. The framework is used as a Business Intelligence Competency Center (BICC) for providing real-time BI solutions to BUs.