Federated Learning (FL) Interoperability, Access and Latency Optimization on The Edge and Cloud

As the underlying networks transition into 5G and 6G infrastructure, the optimal task performance across different WIoT devices with different energy consumption and computing power require coordination at both the software and hardware levels to maximize accessibility and minimize latency to support emerging applications. The proposed research will explore the various parameter space to determine how federated learning should be optimally executed in real-time, in the edge and cloud, to maximize user experience supported by upcoming 6G networks. The proposed research will explore the interoperability scenarios of an optimized Federated Learning model for the company’s digital services & product development strategy with focus on Mobile Edge Networks (MENs). This project aims at providing recommendations of optimum pathways for hardware & software interoperability to integrate WIoT (parameters) with FutureCite company’s digital strategy for a 5G to 6G transition scenarios, and the identification of costs of these optimum pathways for partner company.

Faculty Supervisor:

Scott Yam;Rasha Kashef

Student:

Partner:

FutureCite Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Queen's University; Toronto Metropolitan University

Program: