Through the telecom lens: Are all training samples important?

Shruti Bothe, Illyyne Saffar, Aurelie Boisbunon, Hasan Farooq, Julien Forgeat, Md Moin Uddin Chowdhury

Keywords: sample selection, importance, compute and energy efficiency, data reduction, data optimization, telecom networks

TL;DR: This paper questions the equal-importance assumption by analyzing individual training samples to selectively prioritize impactful data and reduce computational without compromising accuracy

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. This paper questions the equal-importance assumption by analyzing individual sample roles in telecom training and assessing whether the proposed method optimizes computing and energy use. We conduct 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 that selectively prioritizes impactful data and reduce computational without compromising accuracy. Experiments on real-world telecom datasets show our method preserves performance while reducing data needs and computational overhead while advancing the goals of sustainable AI in telecommunications.

Submission Number: 50