:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xu, Chongyang, Siebenbrunner, Christoph, Bindschaedler, Laurent
Format:	Preprint
Published:	2026
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	https://arxiv.org/abs/2602.01872
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

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

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

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

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

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

Graph Neural Networks Gone Hogwild
by: Solodova, Olga, et al.
Published: (2024)

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)

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

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

Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
by: Jiang, Chenyu, et al.
Published: (2024)

Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
by: Lin, Jun-Liang, et al.
Published: (2026)

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

Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows
by: Mohammadi, Bardia, et al.
Published: (2026)

Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
by: Yoon, Daegun, et al.
Published: (2024)

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

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)

Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
by: Lu, Yishun, et al.
Published: (2026)

GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
by: Jeon, Byungsoo, et al.
Published: (2024)

Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training
by: Deng, Yangtao, et al.
Published: (2025)

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

DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
by: Hu, Tianhao, et al.
Published: (2026)

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

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

High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
by: Lu, Fred, et al.
Published: (2024)

AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training
by: Bai, Huawei, et al.
Published: (2025)

BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models
by: Wang, Zhengyang, et al.
Published: (2025)

Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning
by: Zhou, Wenxuan, et al.
Published: (2024)

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

Communication Optimization for Distributed Training: Architecture, Advances, and Opportunities
by: Wei, Yunze, et al.
Published: (2024)

MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training
by: Yoon, Daegun, et al.
Published: (2023)

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
by: Condat, Laurent, 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)

SparDL: Distributed Deep Learning Training with Efficient Sparse Communication
by: Zhao, Minjun, et al.
Published: (2023)