Cover Image

Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kralj, Ivan, Giaretta, Lodovico, Ježić, Gordan, Žarko, Ivana Podnar, Girdzijauskas, Šarūnas
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNs
by: Kralj, Ivan, et al.
Published: (2025)

QEdgeProxy: QoS-Aware Load Balancing for IoT Services in the Computing Continuum
by: Čilić, Ivan, et al.
Published: (2024)

QoS-Aware Load Balancing in the Computing Continuum via Multi-Player Bandits
by: Čilić, Ivan, et al.
Published: (2025)

Inference Load-Aware Orchestration for Hierarchical Federated Learning
by: Lackinger, Anna, et al.
Published: (2024)

Spatiotemporal Traffic Prediction in Distributed Backend Systems via Graph Neural Networks
by: Qiu, Zhimin, et al.
Published: (2025)

Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budget
by: Čilić, Ivan, et al.
Published: (2024)

Semi-decentralized Federated Time Series Prediction with Client Availability Budgets
by: Bao, Yunkai, et al.
Published: (2025)

Heta: Distributed Training of Heterogeneous Graph Neural Networks
by: Zhong, Yuchen, et al.
Published: (2024)

A Cloud-Based Spatio-Temporal GNN-Transformer Hybrid Model for Traffic Flow Forecasting with External Feature Integration
by: Zheng, Zhuo, et al.
Published: (2025)

Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale
by: Fan, Yangxin, et al.
Published: (2024)

Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
by: Li, Jun, et al.
Published: (2024)

LR-CNN: Lightweight Row-centric Convolutional Neural Network Training for Memory Reduction
by: Wang, Zhigang, et al.
Published: (2024)

RapidGNN: Communication Efficient Large-Scale Distributed Training of Graph Neural Networks
by: Niam, Arefin, et al.
Published: (2025)

CaPGNN: Optimizing Parallel Graph Neural Network Training with Joint Caching and Resource-Aware Graph Partitioning
by: Song, Xianfeng, et al.
Published: (2025)

Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-Temporal Graph Learning Method for Traffic Flow Forecasting
by: Wang, Feng, et al.
Published: (2025)

Hypersparse Traffic Matrices from Suricata Network Flows using GraphBLAS
by: Houle, Michael, et al.
Published: (2024)

Towards Scalable GPU-Accelerated SNN Training via Temporal Fusion
by: Li, Yanchen, et al.
Published: (2024)

STAlloc: Enhancing Memory Efficiency in Large-Scale Model Training with Spatio-Temporal Planning
by: Huang, Zixiao, et al.
Published: (2025)

Joint Resource Optimization, Computation Offloading and Resource Slicing for Multi-Edge Traffic-Cognitive Networks
by: Xiaoyang, Ting, et al.
Published: (2024)

MalleTrain: Deep Neural Network Training on Unfillable Supercomputer Nodes
by: Ma, Xiaolong, et al.
Published: (2024)

Message Size Matters: AlterBFT's Approach to Practical Synchronous BFT in Public Clouds
by: Milošević, Nenad, et al.
Published: (2025)

DARIS: An Oversubscribed Spatio-Temporal Scheduler for Real-Time DNN Inference on GPUs
by: Babaei, Amir Fakhim, et al.
Published: (2025)

Semi-Synchronous Exploration in Dynamic Graphs
by: Saxena, Ashish, et al.
Published: (2026)

Optimizing Distributed Training Approaches for Scaling Neural Networks
by: Baligodugula, Vishnu Vardhan, et al.
Published: (2025)

Spatio-Temporal Shifting to Reduce Carbon, Water, and Land-Use Footprints of Cloud Workloads
by: Attenni, Giulio, et al.
Published: (2025)

Fully Distributed Online Training of Graph Neural Networks in Networked Systems
by: Olshevskyi, Rostyslav, et al.
Published: (2024)

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks
by: Zhai, Wenbin, et al.
Published: (2023)

Multi-Resolution Model Fusion for Accelerating the Convolutional Neural Network Training
by: Wang, Kewei, et al.
Published: (2025)

ReInc: Scaling Training of Dynamic Graph Neural Networks
by: Guan, Mingyu, et al.
Published: (2025)

ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks
by: Shi, Ziji, et al.
Published: (2024)

Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction
by: Zhang, Chuanting, et al.
Published: (2025)

CityPulse: Real-Time Traffic Data Analytics and Congestion Prediction
by: Teledjieu, Idriss Djiofack, et al.
Published: (2025)

GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
by: Polisetty, Sandeep, et al.
Published: (2023)

Cluster Topology-Driven Placement of Experts Reduces Network Traffic in MoE Inference
by: Sivtsov, Danil, et al.
Published: (2025)

ATLAS: Efficient Out-of-Core Inference for Billion-Scale Graph Neural Networks
by: Naman, Pranjal, et al.
Published: (2026)

Grappa: Gradient-Only Communication for Scalable Graph Neural Network Training
by: Xu, Chongyang, et al.
Published: (2026)

An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
by: Merkel, Nikolai, et al.
Published: (2023)

Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
by: Wu, Xiabao, et al.
Published: (2025)

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
by: Zhai, Yijun, et al.
Published: (2024)

A Parallel Alternative for Energy-Efficient Neural Network Training and Inferencing
by: Seal, Sudip K., et al.
Published: (2025)