Through the telecom lens: Are all training samples important?

TL;DR

This paper investigates the importance of individual training samples in telecom AI models, proposing a framework to prioritize impactful data, thereby reducing computation and energy use without sacrificing accuracy.

Contribution

It introduces a novel sample importance framework based on gradient analysis, optimizing training efficiency in telecom AI applications.

Findings

Reduces training data and computational costs by prioritizing impactful samples.

Maintains model accuracy while improving sustainability in telecom AI.

Demonstrates effectiveness across three real-world telecom datasets.

Abstract

The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is often noisy, high-dimensional, costly to store, process, and label. Despite Ai's critical role, standard workflows still assume all training samples contribute equally. On the other hand, next generation systems require AI models that are accurate, efficient, and sustainable.The paper questions the assumptions of equal importance by focusing on applying and analyzing the roles of individual samples in telecom training and assessing whether the proposed model optimizes computation and energy use. we perform sample-level gradient analysis across epochs to identify patterns of influence and redundancy in model learning. Based on this, we propose a sample importance framework thats electively prioritizes impactful data and reduces computation without compromising accuracy. Experiments on three real-world telecom datasets show that our method [reserves performance while reducing data needs and computational overhead while advancing the goals of sustainable AI in telecommunications.