Applications of Federated Learning in IoT for Hyper Personalisation

TL;DR

This paper explores how federated learning can be applied to IoT devices to enable highly personalized services without compromising data privacy, leveraging decentralized data processing across billions of devices.

Contribution

It introduces novel methods for implementing hyper-personalization in IoT through federated learning, addressing challenges of data utilization and privacy.

Findings

Federated learning enables effective personalization on IoT devices.

Decentralized training preserves user privacy.

Enhanced personalization levels achieved through FL.

Abstract

Billions of IoT devices are being deployed, taking advantage of faster internet, and the opportunity to access more endpoints. Vast quantities of data are being generated constantly by these devices but are not effectively being utilised. Using FL training machine learning models over these multiple clients without having to bring it to a central server. We explore how to use such a model to implement ultra levels of personalization unlike before