Dr. Gibson Kimutai 

The smart city is a potential technology to address the challenge of population increase by allowing efficient use of resources and managing access to scarce resources optimally. This study addresses the problem of heterogeneous stakeholders and devices in a smart city context, with examples from conventional and smart vehicles, environmental sensors, smart-traffic management, disaster management, and public transportation services in three ways: First, we propose a technology to allow communication of heterogeneous devices with different owners, service managers, and capabilities in a smart city for effectively and flexibly managing those devices. Second, we intend to offload the Machine Learning to run in Edge and Fog environments closer to data creation. This will achieve a truly Federated Learning (FL) model that collaboratively works in the Edge, Fog, and Cloud environments. Third, we will use feasibility studies to validate the framework for large scenarios and in real settings as prototypes for smaller scenarios.