Towards Low-Energy Adaptive Personalization for Resource-Constrained Devices

arXiv:2403.15905v1 Announce Type: new
Abstract: The personalization of machine learning (ML) models to address data drift is a significant challenge in the context of Internet of Things (IoT) applications. Presently, most approaches focus on fine-tuning either the full base model or its last few layers to adapt to new data, while often neglecting energy costs. However, various types of data drift exist, and fine-tuning the full base model or the last few layers may not result in optimal performance in …

abstract adapt applications arxiv challenge context cs.cv cs.lg data devices drift energy fine-tuning focus internet internet of things iot low low-energy machine machine learning personalization type