Image de couverture de livre

Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ochiai, Hideya, Nishihata, Riku, Tomiyama, Eisuke, Sun, Yuwei, Esaki, Hiroshi
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Distributed, Parallel, and Cluster Computing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices
par: Molina, Romina Soledad, et autres
Publié: (2025)

Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems
par: Vasiljevic, Pavle, et autres
Publié: (2025)

Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT
par: Wang, Heqiang, et autres
Publié: (2024)

Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks
par: Singh, Gurpreet, et autres
Publié: (2025)

Federated PCA on Grassmann Manifold for IoT Anomaly Detection
par: Nguyen, Tung-Anh, et autres
Publié: (2024)

QSync: Quantization-Minimized Synchronous Distributed Training Across Hybrid Devices
par: Zhao, Juntao, et autres
Publié: (2024)

NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning
par: Wang, Irene, et autres
Publié: (2026)

Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
par: Perazzone, Jake B., et autres
Publié: (2025)

Peer-to-Peer Deep Learning for Beyond-5G IoT
par: Pranav, Srinivasa, et autres
Publié: (2023)

Energy-aware Incremental OTA Update for Flash-based Batteryless IoT Devices
par: Wei, Wei, et autres
Publié: (2024)

Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices
par: Compaoré, Wilfrid Sougrinoma, et autres
Publié: (2025)

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT
par: Ju, Zehao, et autres
Publié: (2024)

Accelerating Local LLMs on Resource-Constrained Edge Devices via Distributed Prompt Caching
par: Matsutani, Hiroki, et autres
Publié: (2026)

Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
par: Chu, Shunfeng, et autres
Publié: (2024)

Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
par: Rangwala, Murtaza, et autres
Publié: (2025)

DiSCo: Device-Server Collaborative LLM-Based Text Streaming Services
par: Sun, Ting, et autres
Publié: (2025)

LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices
par: Ding, Chuntao, et autres
Publié: (2024)

Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
par: Yan, Jinqi, et autres
Publié: (2025)

FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System
par: Huang, Jianjun, et autres
Publié: (2024)

DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning
par: Wang, Zhiyu, et autres
Publié: (2026)

Future-Proofing IoT: Unleashing the Power of AWS Greengrass in Propelling Smart Devices to New Heights
par: Kokkula, Sahasra, et autres
Publié: (2024)

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks
par: Zhai, Wenbin, et autres
Publié: (2023)

Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
par: Marfo, William, et autres
Publié: (2025)

Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
par: Wu, Zhiyuan, et autres
Publié: (2024)

Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
par: Zhao, Haitao, et autres
Publié: (2026)

Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devices
par: Daghero, Francesco, et autres
Publié: (2024)

Adaptive Stream Processing on Edge Devices through Active Inference
par: Sedlak, Boris, et autres
Publié: (2024)

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things
par: Zhang, Tinghao, et autres
Publié: (2024)

A Robust Federated Learning Framework for Undependable Devices at Scale
par: Wang, Shilong, et autres
Publié: (2024)

Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference
par: Prabhakar, Rohan Baskar, et autres
Publié: (2024)

High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
par: Lu, Fred, et autres
Publié: (2024)

A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
par: Mahmoud, Taha M., et autres
Publié: (2025)

Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
par: Pan, Qiying, et autres
Publié: (2023)

DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs
par: Yao, Xinyu, et autres
Publié: (2025)

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
par: Wang, Haoming, et autres
Publié: (2023)

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
par: Li, Yichen, et autres
Publié: (2024)

Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference
par: Zhang, Haolin, et autres
Publié: (2025)

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
par: Cho, Yae Jee, et autres
Publié: (2024)

ADApt: Edge Device Anomaly Detection and Microservice Replica Prediction
par: Mehran, Narges, et autres
Publié: (2025)

Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
par: Feng, Chao, et autres
Publié: (2025)