Cover Image

ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shi, Ziji, Li, Jialin, You, Yang
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training
by: Tan, Wenting, et al.
Published: (2023)

TAPAS: Fast and Automatic Derivation of Tensor Parallel Strategies for Large Neural Networks
by: Shi, Ziji, et al.
Published: (2023)

Reaching Agreement Among Reasoning LLM Agents
by: Ruan, Chaoyi, et al.
Published: (2025)

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence
by: Chen, Xinquan, et al.
Published: (2026)

Revisiting Parameter Server in LLM Post-Training
by: Wan, Xinyi, et al.
Published: (2026)

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

Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Framework for Server-Light DNN Inference over Massively Distributed Clients via Training-Free Intermediate Feature Compression
by: Sung, Mingyu, et al.
Published: (2025)

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training
by: Ye, Chenhao, et al.
Published: (2026)

Laminar: A Scalable Asynchronous RL Post-Training Framework
by: Sheng, Guangming, et al.
Published: (2025)

Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents
by: Wang, Jialin, et al.
Published: (2024)

Cortex: Achieving Low-Latency, Cost-Efficient Remote Data Access For LLM via Semantic-Aware Knowledge Caching
by: Ruan, Chaoyi, et al.
Published: (2025)

Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
by: Saad, Zainab, et al.
Published: (2025)

TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training
by: Liu, Man, et al.
Published: (2026)

Byzantine-Robust and Communication-Efficient Distributed Training: Compressive and Cyclic Gradient Coding
by: Li, Chengxi, et al.
Published: (2026)

FedAT: Federated Adversarial Training for Distributed Insider Threat Detection
by: Gayathri, R G, et al.
Published: (2024)

Privacy-Preserving Federated Learning: Integrating Zero-Knowledge Proofs in Scalable Distributed Architectures
by: Gupta, Divya
Published: (2026)

Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing
by: Anderson, Derek, et al.
Published: (2026)

HETHUB: A Distributed Training System with Heterogeneous Cluster for Large-Scale Models
by: Xu, Si, et al.
Published: (2024)

Beluga: A CXL-Based Memory Architecture for Scalable and Efficient LLM KVCache Management
by: Yang, Xinjun, et al.
Published: (2025)

PRAGMA: A Profiling-Reasoned Multi-Agent Framework for Automatic Kernel Optimization
by: Lei, Kelun, et al.
Published: (2025)

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning
by: Wang, Yisu, et al.
Published: (2025)

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation
by: Kaplan, Erel, et al.
Published: (2026)

PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
by: Wan, Xinyi, et al.
Published: (2025)

DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systems
by: Zhou, Wenqing, et al.
Published: (2025)

ModTrans: Translating Real-world Models for Distributed Training Simulator
by: Lyu, Yi
Published: (2026)

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

EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models
by: Cheng, Jialiang, et al.
Published: (2024)

MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
by: Yu, Dianhai, et al.
Published: (2022)

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

OrchMLLM: Orchestrate Multimodal Data with Batch Post-Balancing to Accelerate Multimodal Large Language Model Training
by: Zheng, Yijie, et al.
Published: (2025)

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks
by: Xian, Youquan, et al.
Published: (2023)

Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
by: Firoz, Jesun, et al.
Published: (2025)

Placement Semantics for Distributed Deep Learning: A Systematic Framework for Analyzing Parallelism Strategies
by: Mehta, Deep Pankajbhai
Published: (2026)

Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems
by: Lu, Ning, et al.
Published: (2024)

Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
by: Zhu, Zhanda, et al.
Published: (2025)

Accelerating Long-Tail Generation in Synchronous RLHF Training via Adaptive Tensor Parallelism
by: Zhao, Long, et al.
Published: (2026)

DeInfoReg: A Decoupled Learning Framework for Better Training Throughput
by: Huang, Zih-Hao, et al.
Published: (2025)

ECCENTRIC: Edge-Cloud Collaboration Framework for Distributed Inference Using Knowledge Adaptation
by: Kamani, Mohammad Mahdi, et al.
Published: (2025)

AIBrix: Towards Scalable, Cost-Effective Large Language Model Inference Infrastructure
by: The AIBrix Team, et al.
Published: (2025)

Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks
by: Lee, Hunmin, et al.
Published: (2024)