  The Internet of Things has experienced significant growth and has become an
integral part of various industries. This expansion has given rise to the
Industrial IoT initiative where industries are utilizing IoT technology to
enhance communication and connectivity through innovative solutions such as
data analytics and cloud computing. However this widespread adoption of IoT is
demanding of algorithms that provide better efficiency for the same training
environment without speed being a factor. In this paper we present a novel
approach called G Federated Proximity. Building upon the existing FedProx
technique our implementation introduces slight modifications to enhance its
efficiency and effectiveness. By leveraging FTL our proposed system aims to
improve the accuracy of model obtained after the training dataset with the help
of normalization techniques such that it performs better on real time devices
and heterogeneous networks Our results indicate a significant increase in the
throughput of approximately 90% better convergence compared to existing model
performance.
