Title: Federated Learning for Privacy-Preserving IoT Analytics

Authors:  Dr. Eleanor Christopher

Abstract: The increasing deployment of Internet of Things (IoT) devices generates vast amounts of data, offering valuable insights for various applications. However, traditional centralized data analytics approaches raise significant privacy concerns as sensitive data from distributed IoT devices are often aggregated and processed in a central server. Federated learning (FL) has emerged as a promising solution to address these privacy challenges by enabling collaborative model training on decentralized data sources without direct data sharing. This research investigates the application of FL techniques for privacy-preserving IoT analytics. We explore various FL algorithms, including variations of federated averaging and personalized federated learning, to train machine learning models on data residing on individual IoT devices. The goal is to extract meaningful insights and perform accurate predictions while preserving the privacy of sensitive information. We address the challenges of heterogeneity in IoT devices and data distributions, exploring techniques like model personalization and robust aggregation methods. Furthermore, we evaluate the performance of different FL approaches in terms of model accuracy, communication efficiency, and privacy guarantees. The results demonstrate the potential of FL to enable collaborative analytics in IoT environments while safeguarding sensitive data. This research contributes to the growing body of knowledge in privacy-preserving machine learning and offers insights into building secure and intelligent IoT systems.

Keywords:Federated Learning (FL), Internet of Things (IoT), Privacy-Preserving, Decentralized Learning, Distributed Data, Machine Learning

International Journal of Applied Pattern Recognition, Vol. 5, No. 1, 2017 

Received: 14 Feb 2017

Accepted: 15 April 2017

Published online: 11 June 2017