Copertina

FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nguyen, Van Tuan, Beuran, Razvan
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

CST-AFNet: A dual attention-based deep learning framework for intrusion detection in IoT networks
di: Ishtiaq, Waqas, et al.
Pubblicazione: (2025)

Fake detection in imbalance dataset by Semi-supervised learning with GAN
di: Bordbar, Jinus, et al.
Pubblicazione: (2022)

FedStale: leveraging stale client updates in federated learning
di: Rodio, Angelo, et al.
Pubblicazione: (2024)

FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
di: Wang, Kaile, et al.
Pubblicazione: (2026)

Beyond Binary Classification: A Semi-supervised Approach to Generalized AI-generated Image Detection
di: Nguyen-Le, Hong-Hanh, et al.
Pubblicazione: (2025)

Federated PCA on Grassmann Manifold for IoT Anomaly Detection
di: Nguyen, Tung-Anh, et al.
Pubblicazione: (2024)

FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions
di: Dave, Darpit, et al.
Pubblicazione: (2024)

Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
di: van Nieuwkoop, Gijs, et al.
Pubblicazione: (2026)

Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
di: Gryech, Ihsane, et al.
Pubblicazione: (2024)

Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review
di: Kheddar, Hamza, et al.
Pubblicazione: (2023)

SemiReward: A General Reward Model for Semi-supervised Learning
di: Li, Siyuan, et al.
Pubblicazione: (2023)

Quantifying Catastrophic Forgetting in IoT Intrusion Detection Systems
di: Banerjee, Sourasekhar, et al.
Pubblicazione: (2026)

TinyML-Enabled IoT for Sustainable Precision Irrigation
di: Taueatsoala, Kamogelo, et al.
Pubblicazione: (2026)

Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems
di: Dinh, Phai Vu, et al.
Pubblicazione: (2024)

DevPiolt: Operation Recommendation for IoT Devices at Xiaomi Home
di: Wang, Yuxiang, et al.
Pubblicazione: (2025)

Enhancing Semi-supervised Learning with Zero-shot Pseudolabels
di: Chung, Jichan, et al.
Pubblicazione: (2025)

A Unified Framework for Heterogeneous Semi-supervised Learning
di: Heidari, Marzi, et al.
Pubblicazione: (2025)

Graph Neural Diffusion Networks for Semi-supervised Learning
di: Ye, Wei, et al.
Pubblicazione: (2022)

Meta-learning how to Share Credit among Macro-Actions
di: Hosu, Ionel-Alexandru, et al.
Pubblicazione: (2025)

Defect detection using weakly supervised learning
di: Sevetlidis, Vasileios, et al.
Pubblicazione: (2023)

Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology
di: Rjoub, Gaith, et al.
Pubblicazione: (2024)

Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions
di: Li, Zuguang, et al.
Pubblicazione: (2024)

Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals
di: Kimura, Tomoyoshi, et al.
Pubblicazione: (2026)

Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments
di: Kanneganti, Deepak, et al.
Pubblicazione: (2025)

Deep evolving semi-supervised anomaly detection
di: Belham, Jack, et al.
Pubblicazione: (2024)

AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
di: Lu, Weigang, et al.
Pubblicazione: (2024)

SECRET: Semi-supervised Clinical Trial Document Similarity Search
di: Das, Trisha, et al.
Pubblicazione: (2025)

Semi-supervised CAPP Transformer Learning via Pseudo-labeling
di: Gross, Dennis, et al.
Pubblicazione: (2026)

Recommending Pre-Trained Models for IoT Devices
di: Patil, Parth V., et al.
Pubblicazione: (2024)

Foundation Models for CPS-IoT: Opportunities and Challenges
di: Baris, Ozan, et al.
Pubblicazione: (2025)

DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning
di: Han, Lixiang, et al.
Pubblicazione: (2024)

Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT Environments
di: Shaer, Ibrahim, et al.
Pubblicazione: (2024)

A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
di: Lin, Xiachong, et al.
Pubblicazione: (2024)

Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
di: Sponner, Max, et al.
Pubblicazione: (2024)

Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
di: Li, Hanzhe, et al.
Pubblicazione: (2024)

Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks
di: Kaur, Navneet, et al.
Pubblicazione: (2025)

An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
di: Tajgardan, Mohsen, et al.
Pubblicazione: (2026)

Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
di: Belay, Mohammed Ayalew, et al.
Pubblicazione: (2026)

(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems
di: Li, Aohan, et al.
Pubblicazione: (2025)

FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes
di: Wang, Zexiao, et al.
Pubblicazione: (2025)