The paper introduces an unsupervised peer-relative representation learning framework to detect energy inefficiencies in mobile network sites. It extends Minimum Distortion Embedding with an energy-based repulsion term, creating a low-dimensional space that preserves structural similarity while highlighting sites with anomalously high energy use. The resulting anomaly scores help operators prioritize field investigations and achieve large-scale energy-efficiency optimization.
By Eliud Nyakweba Koto, Jaco du Toit, Adham Stoltz, Johan du Preez
arXiv:2608. 03087v1 Announce Type: cross Abstract: Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment.
By Lin Jiang, Dahai Yu, Ravikumar Gelli, Guang Wang
Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such data is often restricted by privacy concerns and data-sharing constraints, motivating growing interest in synthetic energy data generation.
arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.
By Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman
arXiv:2608. 09596v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) suffer from two fundamental limitations: over-smoothing, where node representations become indistinguishable with depth, and over-squashing, where long-range information is compressed through limited message-passing channels.
By Killian Cressant, Pedro B. Velloso
The paper investigates the environmental impact of running large language models (LLMs) on mobile devices. It evaluates 18 different LLM configurations on two smartphones and a server, measuring energy per token, latency, accuracy, and battery-cycle consumption. Findings reveal that on-device inference is about three times less energy‑efficient than batched server inference, that energy consumption varies non‑monotonically with quantization bit‑width, and that most models are not on the Pareto front of accuracy and energy efficiency. The study concludes that local AI is not inherently more sustainable than cloud inference, with the majority of environmental impact stemming from device embodied carbon.
By \'Edouard Gu\'egain, Tristan Coignion
The paper titled "The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting" discusses how highly accurate energy forecasting models can paradoxically cause a net energy deficit on edge devices due to inference energy consumption and battery aging. It introduces a Total Cost of Ownership (TCO) framework that unifies inference energy and battery degradation as forms of energy loss, aiming to minimize net energy loss. Experiments show that in thermally sensitive edge environments, the energy saved by more precise, complex models is often offset by the higher operational intensity they require.
By Jaeik Jeong, Tai-Yeon Ku, Wan-Ki Park
arXiv:2607. 17747v1 Announce Type: cross Abstract: The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management.
By Maxime Bouton, Ioanna Mitsioni, Simon Lindst{\aa}hl, Jaeseong Jeong
The paper presents a machine learning method to estimate the CPU-package power of individual virtual servers using only metrics observable within the server. Training uses host-side RAPL measurements, while inference relies solely on in‑guest data. Experiments across diverse CPUs, workloads, and configurations show strong predictions when training data match deployment conditions, with some limitations in zero‑shot scenarios, and the approach adds minimal overhead.
By Amandip Sangha
arXiv:2609.09662v1 Announce Type: cross
Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased pr...
By Weisi Yang, Stephen Xia
arXiv:2606. 23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck.
By Bohua Zou, Nian Liu, Binqi Sun, Matteo Mascherin, Debayan Roy, Yutao Liu, Yu Peng, Ning Jia, Haibo Chen
arXiv:2607. 29659v1 Announce Type: new Abstract: Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices.
By Idan Roth, Lutz Lampe