arXiv Machine Learning

APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations

arXiv:2606. 11553v1 Announce Type: new Abstract: Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol layers.

arXiv Machine Learning

Jun 11

NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series

arXiv:2510. 22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operations demand -- forecasting future load, diagnosing and characterizing anomalies, and searching for and retrieving historical precedents -- requires more than raw measurements.

By Satyandra Guthula, Jaber Daneshamooz, Charles Fleming, Kesheng Wu, Walter Willinger, Arpit Gupta

benchmarks safety

arXiv Machine Learning

Aug 4

Learned Digital Over-the-Air Computing for Federated Edge Learning

arXiv:2509. 16577v2 Announce Type: replace Abstract: Over-the-air (OTA) aggregation enables federated edge learning (FEEL) by exploiting the superposition property of the wireless channel to merge communication with computation, eliminating the need to schedule and decode devices individually.

By Antonio Tarizzo, Mohammad Kazemi, Deniz G\"und\"uz

benchmarks

arXiv Machine Learning

Jun 9

Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum

arXiv:2606. 09787v1 Announce Type: new Abstract: The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proactive Zero Touch Management via time-series forecasting essential.

By Abd Elghani Meliani, Arora Sagar, Adlen Ksentini, Raymond Knopp

agents efficiency

arXiv Machine Learning

Aug 4

CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives

arXiv:2608. 01725v1 Announce Type: cross Abstract: Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon.

By Zifan Zhang, Zhichao Hou, Tingxiang Ji, Yuchen Liu

arXiv AI

Jul 20

FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels

arXiv:2607. 15469v1 Announce Type: cross Abstract: Federated Learning (FL) over 5G cellular networks protects raw data but remains vulnerable to side-channel leakage.

By Md Nahid Hasan Shuvo, Mahmudul Hassan Ashik, Moinul Hossain

llms

arXiv AI

2d ago

A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks

arXiv:2608. 14694v1 Announce Type: new Abstract: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks.

By Naveed Khan, Besan Al Sbeihi, Maryam Alshehhi, Nasir Saeed

efficiency benchmarks safety

arXiv AI

Jun 6

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN

arXiv:2606. 06261v1 Announce Type: cross Abstract: O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces.

By Francesco Spinelli, Esteban Municio, Pau Baguer, Gines Garcia-Aviles, Xavier Costa-Perez

llms agents multimodal benchmarks

Hugging Face Trending Papers

Aug 4

Predicting Deep Neural Network Training Outcomes from Early Training Telemetry

Large hyperparameter sweeps for deep neural networks spend substantial compute on configurations that are effectively doomed from the first few epochs. We study whether a single training run's own early telemetry - per-epoch loss, training accuracy, gradient signal-to-noise ratio, weight-norm growth, and an activation-saturation snapshot - together with its sampled hyperparameters, can predict that run's eventual outcome without reference to other runs.

benchmarks

arXiv Machine Learning

Jul 13

Federated Low-Rank Koopman Learning for Multivariate Time-Series Anomaly Detection in IoT Systems

arXiv:2607. 08978v1 Announce Type: new Abstract: Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms.

By Tung-Anh Nguyen, Van-Phuc Bui, Anh Tuyen Le, Kim Hue Ta, Minh Thuy Le, J. Andrew Zhang, Xiaojing Huang

efficiency benchmarks

arXiv Machine Learning

Aug 7

Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining

arXiv:2608. 05605v1 Announce Type: cross Abstract: Research and Education Networks (RENs) serve as critical infrastructure for scientific discovery, yet they face a unique security paradox: their normal traffic patterns which are characterized by massive, bursty "elephant flows" are statistically indistinguishable from volumetric attacks such as DDoS to conventional monitoring systems.

By Mohammad Arafath Uddin Shariff, Byrav Ramamurthy

agents benchmarks

arXiv AI

Jul 2

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.

By Jinju Park, Seokho Kang

llms rag benchmarks

arXiv AI

Jun 3

WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition

arXiv:2606. 02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.

By Maheen Arshad, Qindeel E Zahra, Muhammad Khuram Shahzad

safety

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