Applications of Federated Learning in IoT for Hyper Personalisation
This addresses the challenge of leveraging IoT data for personalization in a privacy-preserving manner, but it appears incremental as it explores existing federated learning methods in a specific context.
The paper tackles the problem of underutilized data from billions of IoT devices by applying federated learning to train machine learning models across multiple clients without centralizing data, aiming to achieve unprecedented levels of personalization.
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