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.

翻译：物联网已实现显著增长，并成为各行业不可或缺的组成部分。这一扩张催生了工业物联网计划，各行业正利用物联网技术，通过数据分析和云计算等创新解决方案来增强通信与连接能力。然而，物联网的广泛部署对算法提出了更高要求，需要在相同训练环境下提供更优效率，且不以牺牲速度为代价。本文提出一种名为G Federated Proximity的新方法。该方法基于现有FedProx技术，通过引入细微改进以提升其效率与有效性。借助FTL技术，我们提出的系统旨在通过归一化等技术提升训练数据集所得模型的准确性，使其在实时设备与异构网络中表现更优。实验结果表明，与现有模型性能相比，该系统吞吐量显著提升，收敛速度提高约90%。