Image de couverture de livre

Cooperative SGD with Dynamic Mixing Matrices

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Sarkar, Soumya, Jain, Shweta
Format:	Preprint
Publié:	2025
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

Ordered Momentum for Asynchronous SGD
par: Shi, Chang-Wei, et autres
Publié: (2024)

Truncated Non-Uniform Quantization for Distributed SGD
par: Yan, Guangfeng, et autres
Publié: (2024)

Birch SGD: A Tree Graph Framework for Local and Asynchronous SGD Methods
par: Tyurin, Alexander, et autres
Publié: (2025)

SignSGD with Federated Voting
par: Park, Chanho, et autres
Publié: (2024)

MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times
par: Maranjyan, Artavazd, et autres
Publié: (2024)

LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
par: Maziane, Yassine, et autres
Publié: (2026)

Cooperative Minibatching in Graph Neural Networks
par: Balin, Muhammed Fatih, et autres
Publié: (2023)

Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
par: Zhang, Zhenxiao, et autres
Publié: (2024)

Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity
par: Maranjyan, Artavazd, et autres
Publié: (2025)

Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
par: Luo, Ruichen, et autres
Publié: (2025)

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data
par: Maranjyan, Artavazd
Publié: (2026)

TDC-Cache: A Trustworthy Decentralized Cooperative Caching Framework for Web3.0
par: Chen, Jinyu, et autres
Publié: (2025)

NOMAD: Generating Embeddings for Massive Distributed Graphs
par: Sarkar, Aishwarya, et autres
Publié: (2026)

Fair Distributed Cooperative Bandit Learning on Networks for Intelligent Internet of Things Systems (Technical Report)
par: Chen, Ziqun, et autres
Publié: (2024)

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

The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
par: Patel, Kumar Kshitij, et autres
Publié: (2024)

Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference
par: Siavashi, Mohammad, et autres
Publié: (2025)

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
par: Mahran, Ammar, et autres
Publié: (2026)

Hierarchical Federated Learning with SignSGD: A Highly Communication-Efficient Approach
par: Kazemi, Amirreza, et autres
Publié: (2026)

HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference
par: Tang, Peng, et autres
Publié: (2024)

Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixing
par: Touat, Ousmane, et autres
Publié: (2024)

FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters
par: Ishii, Hiro, et autres
Publié: (2025)

pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup
par: Saadati, Yasaman, et autres
Publié: (2025)

AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
par: Wang, Zhiyu, et autres
Publié: (2025)

Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching
par: Peng, Jie, et autres
Publié: (2024)

Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity
par: Maranjyan, Artavazd, et autres
Publié: (2025)

HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction
par: Sarkar, Aishwarya, et autres
Publié: (2025)

DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
par: Jiang, Chenyu, et autres
Publié: (2025)

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

Dynamic Rebatching for Efficient Early-Exit Inference with DREX
par: Liu, Xuting, et autres
Publié: (2025)

DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
par: Zhao, Xuanlei, et autres
Publié: (2024)

ReInc: Scaling Training of Dynamic Graph Neural Networks
par: Guan, Mingyu, et autres
Publié: (2025)

DHP: Efficient Scaling of MLLM Training with Dynamic Hybrid Parallelism
par: Niu, Yifan, et autres
Publié: (2026)

Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
par: Dorfman, Ron, et autres
Publié: (2024)

DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
par: Yang, Xu, et autres
Publié: (2024)

Dynamic Topology Optimization for Non-IID Data in Decentralized Learning
par: Cox, Bart, et autres
Publié: (2026)

FedMef: Towards Memory-efficient Federated Dynamic Pruning
par: Huang, Hong, et autres
Publié: (2024)

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning
par: Guo, Shunxin, et autres
Publié: (2025)

Kareus: Joint Reduction of Dynamic and Static Energy in Large Model Training
par: Wu, Ruofan, et autres
Publié: (2026)

Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
par: Zhao, Yi, et autres
Publié: (2026)