Copertina

BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhu, Heng, Ling, Qing
Natura:	Preprint
Pubblicazione:	2021
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

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

Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
di: Li, Junyi, et al.
Pubblicazione: (2023)

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

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

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

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

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

Efficient Adaptive Federated Optimization
di: Lee, Su Hyeong, et al.
Pubblicazione: (2024)

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

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

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

Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
di: Bylinkin, Dmitry, et al.
Pubblicazione: (2024)

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

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

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

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

HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
di: Su, Weijie J., et al.
Pubblicazione: (2018)

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

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

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

Communication-efficient Vertical Federated Learning via Compressed Error Feedback
di: Valdeira, Pedro, et al.
Pubblicazione: (2024)

A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
di: Shi, Xinli, et al.
Pubblicazione: (2025)

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

CONGO: Compressive Online Gradient Optimization
di: Carleton, Jeremy, et al.
Pubblicazione: (2024)

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

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

Activations and Gradients Compression for Model-Parallel Training
di: Rudakov, Mikhail, et al.
Pubblicazione: (2024)

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)

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

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

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

Continuous-Time Analysis of Federated Averaging
di: Overman, Tom, et al.
Pubblicazione: (2025)

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

A Bias-Correction Decentralized Stochastic Gradient Algorithm with Momentum Acceleration
di: Hu, Yuchen, 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)

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

Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method
di: Mhanna, Elissa, et al.
Pubblicazione: (2024)

GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
di: Maranjyan, Artavazd, et al.
Pubblicazione: (2022)

Communication Efficient Distributed Training with Distributed Lion
di: Liu, Bo, et al.
Pubblicazione: (2024)

LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
di: Maziane, Yassine, et al.
Pubblicazione: (2026)