Augmenting the FedProx Algorithm by Minimizing Convergence
This work addresses the need for more efficient federated learning algorithms in Industrial IoT settings, though it appears incremental as it builds directly on FedProx.
The paper tackles the problem of improving efficiency in Industrial IoT environments by proposing G Federated Proximity, a modified version of FedProx, which achieves approximately 90% better convergence throughput compared to existing models.
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.