Optimizing Cloud Deployment and Federated Learning Using HECFO Algorithm

The advancements of cloud and Artificial Intelligence technology, enhancements in their integrations have become central to enhancing efficiency, scalability, and efficacious resource utilization. Classic federated learning (FL) models have essentially been developed based on centralized cloud frameworks. This can lead to very drastic implications, such as high communication latency, channel width constraints, and even inability to model grouping effectively. The proposed concept is Hierarchical Edge Cloud Federated Optimization: A scheme that caters shall increasingly improve cloud deployments and Federated Learning by introducing structured multilayers in processing. The transfer and aggregation of model updates will occur in-edge computing locally, acting on the model before they are dumped to the cloud; therefore, this transit will cause less computational overhead and high network efficiency. This hierarchical scheme shall further accelerate federated learning convergence and optimize cloud resource allocation for cost-effective and scalable deployment. For various IoT, healthcare, and smart city infrastructures, this represents HECFO's very significant cloud performance and federated model training improvement through minimization of data transfer and enhancing its real-time processing capabilities like Engaging Edge Computing, Resource Optimization, Distributed AI, Cloud Optimization, and Federated Learning.