Copertina

Energy-efficient Decentralized Learning via Graph Sparsification

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Xusheng, Chiu, Cho-Chun, He, Ting
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Distributed, Parallel, and Cluster Computing Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning
di: Zhang, Xusheng, et al.
Pubblicazione: (2025)

Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering
di: Zehtabi, Shahryar, et al.
Pubblicazione: (2022)

Communication-Efficient Federated Optimization over Semi-Decentralized Networks
di: Wang, He, et al.
Pubblicazione: (2023)

Decentralized Directed Collaboration for Personalized Federated Learning
di: Liu, Yingqi, et al.
Pubblicazione: (2024)

Decentralized Personalized Federated Learning for Min-Max Problems
di: Borodich, Ekaterina, et al.
Pubblicazione: (2021)

Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
di: Zhou, Yuan, et al.
Pubblicazione: (2025)

Optimizing Stochastic Gradient Push under Broadcast Communications
di: Nguyen, Tuan, et al.
Pubblicazione: (2026)

A Single-Loop Algorithm for Decentralized Bilevel Optimization
di: Dong, Youran, et al.
Pubblicazione: (2023)

A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks
di: Yau, Chung-Yiu, et al.
Pubblicazione: (2024)

A Double Tracking Method for Optimization with Decentralized Generalized Orthogonality Constraints
di: Wang, Lei, et al.
Pubblicazione: (2024)

A Bias-Correction Decentralized Stochastic Gradient Algorithm with Momentum Acceleration
di: Hu, Yuchen, et al.
Pubblicazione: (2025)

A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming
di: Nazari, Parvin, et al.
Pubblicazione: (2022)

S$^3$LDBO: A Snapshot Single-Loop Algorithm for Decentralized Bilevel Optimization
di: Yin, Chao, et al.
Pubblicazione: (2026)

FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
di: Wu, Feijie, et al.
Pubblicazione: (2024)

The Privacy Power of Correlated Noise in Decentralized Learning
di: Allouah, Youssef, et al.
Pubblicazione: (2024)

Birch SGD: A Tree Graph Framework for Local and Asynchronous SGD Methods
di: Tyurin, Alexander, et al.
Pubblicazione: (2025)

Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
di: Chen, Xiaojing, et al.
Pubblicazione: (2024)

Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
di: He, Yutong, et al.
Pubblicazione: (2023)

Local Methods with Adaptivity via Scaling
di: Chezhegov, Savelii, et al.
Pubblicazione: (2024)

On Biased Compression for Distributed Learning
di: Beznosikov, Aleksandr, et al.
Pubblicazione: (2020)

On Principled Local Optimization Methods for Federated Learning
di: Yuan, Honglin
Pubblicazione: (2024)

Adaptive Federated Learning with Auto-Tuned Clients
di: Kim, Junhyung Lyle, et al.
Pubblicazione: (2023)

Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
di: Huang, Yan, et al.
Pubblicazione: (2024)

Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
di: He, Yutong, et al.
Pubblicazione: (2023)

Stochastic Controlled Averaging for Federated Learning with Communication Compression
di: Huang, Xinmeng, et al.
Pubblicazione: (2023)

Online Distributed Learning with Quantized Finite-Time Coordination
di: Bastianello, Nicola, et al.
Pubblicazione: (2023)

Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
di: Tyurin, Alexander, et al.
Pubblicazione: (2025)

The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
di: Patel, Kumar Kshitij, et al.
Pubblicazione: (2024)

Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
di: Xiang, Ming, et al.
Pubblicazione: (2024)

Utilizing Graph Sparsification for Pre-processing in Maxcut QUBO Solver
di: Suppakitpaisarn, Vorapong, et al.
Pubblicazione: (2024)

Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
di: Tyurin, Alexander
Pubblicazione: (2025)

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
di: Condat, Laurent, et al.
Pubblicazione: (2024)

GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models
di: Dimlioglu, Tolga, et al.
Pubblicazione: (2024)

ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
di: Maranjyan, Artavazd, et al.
Pubblicazione: (2025)

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
di: Tovmasyan, Zhirayr, et al.
Pubblicazione: (2026)

BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning
di: Zhu, Heng, et al.
Pubblicazione: (2021)

FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
di: Zhou, Yuan, et al.
Pubblicazione: (2025)

Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
di: Sun, Yan, et al.
Pubblicazione: (2023)

AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference for Preconditioning Matrix
di: Yue, Yun, et al.
Pubblicazione: (2023)

CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence
di: Huang, Kun, et al.
Pubblicazione: (2023)