On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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10 results for “IIoT”

FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applicationscs.LG

FDA3 defends IIoT applications against adversarial attacks by federating defense knowledge.

problem Adversarial attacks on DNNs in IIoT applications can cause devastating consequences.

method Federated learning approach to aggregate defense knowledge from different sources.

result FDA3 can resist more attacks than existing methods and prevent new attacks.

federated learning adversarial attacks IIoT federated defense deep neural networks

2020-06-28abs ↗pdf ↗

Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoTcs.LG

The paper proposes an AI and IIoT framework for improved maintenance.

problem Current maintenance practices need improvement with AI and IIoT.

method Review of reliability modeling, introduction of Intelligent Maintenance framework, and novel probabilistic deep learning approach.

result Demonstrated novel probabilistic deep learning reliability modelling in Turbofan Engine Degradation Dataset.

AI IIoT Predictive Maintenance Intelligent Maintenance Reliability Modelling

2020-09-01abs ↗pdf ↗

Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligencecs.LG

New AI method improves anomaly detection across different IIoT sensors.

problem Poor performance of anomaly detection models when applied to different machines.

method Robust AI method using pre-processing and multiple models on different pumps.

result Models perform well across different environments and types of pumps.

AI anomaly detection IIoT sensors transferable models pre-processing centrifugal pumps

2021-10-07abs ↗pdf ↗

Prune2Edge: A Multi-Phase Pruning Pipelines to Deep Ensemble Learning in IIoTcs.LG

Paper proposes a multi-phase pruning pipeline for deep ensemble learning on IIoT devices.

problem Computational limitations of IoT devices for deep learning models.

method Generates diverse pruned models, applies integer quantization, and uses clustering-based pruning.

result Significant reduction in model size (up to 90%) and improved performance (up to 7%) on IIoT devices.

IIoT Edge Intelligence Deep Learning Pruning Quantization

2020-04-09abs ↗pdf ↗

Multi-Dimensional Self Attention based Approach for Remaining Useful Life Estimationcs.LG

This paper proposes a multi-head attention model for predicting RUL in IIoT environments.

problem Estimating RUL for complex industrial equipment using IIoT data.

method Multi-Head Attention Mechanism combined with LSTM for multi-dimensional time-series data.

result The proposed model outperforms state-of-the-art models on benchmark datasets.

RUL prediction IIoT Multi-Head Attention LSTM Prognostics

2022-12-12abs ↗pdf ↗

Continual Learning at the Edge: An Agnostic IIoT Architecturestat.ML

Edge computing tackles dynamic data in IIoT with incremental learning.

problem Latency and bandwidth limitations in IoT devices.

method Incremental learning applied to edge-computing systems for continual learning.

result Reduces catastrophic forgetting and provides efficient real-time quality control.

Edge computing IIoT Incremental learning Catastrophic forgetting Real-time quality control

2025-12-16abs ↗pdf ↗

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcs.LG

This paper proposes a communication-efficient deep anomaly detection framework for industrial IoT.

problem Accurately detecting anomalies in time-series data from edge devices in industrial IoT.

method A federated learning-based approach with an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model and gradient compression.

result The proposed framework accurately and timely detects anomalies with reduced communication overhead.

federated learning anomaly detection time-series data edge devices industrial IoT

2020-07-19abs ↗pdf ↗

AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processescs.LG

This paper proposes AI-based solutions for optimizing semiconductor manufacturing processes.

problem Optimizing semiconductor manufacturing processes with advanced analytics.

method Evolutionary Computing and Deep Learning algorithms for feature selection and neural networks.

result Advanced algorithm for intelligent feature selection in semiconductor manufacturing.

Artificial Intelligence Deep Learning Feature Selection Evolutionary Computing Smart Manufacturing

2020-08-29abs ↗pdf ↗

Anomaly Detection for Industrial Big Datacs.LG

As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure…

2018-04-09abs ↗pdf ↗

Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Servicescs.LG

PSO optimizes hyperparameters for edge ML models in FL.

problem Efficient local training and inference at edge devices.

method Particle Swarm Optimization (PSO) for hyperparameter tuning.

result PSO reduces the number of communication rounds by 2 orders of magnitude.

Federated Learning Edge Computing Hyperparameter Optimization PSO Machine Learning

2020-09-05abs ↗pdf ↗