Cover Image

Graph Neural Networks Gone Hogwild

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Solodova, Olga, Richardson, Nick, Oktay, Deniz, Adams, Ryan P.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Cooperative Minibatching in Graph Neural Networks
by: Balin, Muhammed Fatih, et al.
Published: (2023)

Graph Neural Networks as Ordering Heuristics for Parallel Graph Coloring
by: Langedal, Kenneth, et al.
Published: (2024)

Federated Learning on Stochastic Neural Networks
by: Tang, Jingqiao, et al.
Published: (2025)

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

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

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

Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch
by: Bajaj, Saurabh, et al.
Published: (2024)

Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
by: Yan, Xueming, et al.
Published: (2024)

Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement
by: Makris, Antonios, et al.
Published: (2024)

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
by: Naman, Pranjal, et al.
Published: (2025)

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)

FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
by: Ambekar, Siddharth, et al.
Published: (2024)

SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
by: Huang, Xin, et al.
Published: (2024)

Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
by: Waleffe, Roger, et al.
Published: (2025)

High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction
by: Chen, Zehuan, et al.
Published: (2025)

GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU
by: Yu, Zhongming, et al.
Published: (2024)

CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction
by: Zhang, Shuai, et al.
Published: (2024)

EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
by: Merkel, Nikolai, et al.
Published: (2026)

Distributed Matrix-Based Sampling for Graph Neural Network Training
by: Tripathy, Alok, et al.
Published: (2023)

Leveraging Neural Graph Compilers in Machine Learning Research for Edge-Cloud Systems
by: Furutanpey, Alireza, et al.
Published: (2025)

Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
by: Yu, Yingchao, et al.
Published: (2024)

Vanishing Variance Problem in Fully Decentralized Neural-Network Systems
by: Tian, Yongding, et al.
Published: (2024)

Distributed Convolutional Neural Network Training on Mobile and Edge Clusters
by: Rama, Pranav, et al.
Published: (2024)

BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling
by: Ruschel, Raphael, et al.
Published: (2023)

GPU-Accelerated Optimization of Transformer-Based Neural Networks for Real-Time Inference
by: Mukherjee, Soutrik, et al.
Published: (2026)

Adaptive Compression in Federated Learning via Side Information
by: Isik, Berivan, et al.
Published: (2023)

iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations
by: Anik, Md Saidul Hoque, et al.
Published: (2024)

Enhancing Physics-Informed Neural Networks with a Hybrid Parallel Kolmogorov-Arnold and MLP Architecture
by: Xu, Zuyu, et al.
Published: (2025)

Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling
by: Barwey, Shivam, et al.
Published: (2024)

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

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
by: Chen, Yuanyuan, et al.
Published: (2022)

Conflict-Free Replicated Data Types for Neural Network Model Merging: A Two-Layer Architecture Enabling CRDT-Compliant Model Merging Across 26 Strategies
by: Gillespie, Ryan
Published: (2026)

MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via Automating Deep Neural Network Porting for Mobile Deployment
by: Huang, Hongtao, et al.
Published: (2024)

Federated Temporal Graph Clustering
by: Zhou, Zihao, et al.
Published: (2024)

Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction
by: Kralj, Ivan, et al.
Published: (2024)

D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks
by: Guliyev, Rustam, et al.
Published: (2024)

A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
by: Zhang, Shichang, et al.
Published: (2023)

Edge-Parallel Graph Encoder Embedding
by: Lubonja, Ariel, et al.
Published: (2024)

Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024)