Imagen de Portada

Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Jin, Richeng, Huang, Yufan, He, Xiaofan, Dai, Huaiyu, Wu, Tianfu
Formato:	Preprint
Publicado:	2020
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Distribution-Aware Mobility-Assisted Decentralized Federated Learning
por: Reza, Md Farhamdur, et al.
Publicado: (2025)

Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity
por: Pervej, Md Ferdous, et al.
Publicado: (2023)

Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach
por: Jahani, Reza, et al.
Publicado: (2025)

TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
por: Jin, Richeng, et al.
Publicado: (2024)

Gradient Compression May Hurt Generalization: A Remedy by Synthetic Data Guided Sharpness Aware Minimization
por: Gu, Yujie, et al.
Publicado: (2026)

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
por: Yue, Kai, et al.
Publicado: (2025)

SignSGD with Federated Voting
por: Park, Chanho, et al.
Publicado: (2024)

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models
por: Yu, Dingzhi, et al.
Publicado: (2026)

GSBA$^K$: $top$-$K$ Geometric Score-based Black-box Attack
por: Reza, Md Farhamdur, et al.
Publicado: (2025)

Conceal, Reconstruct, Jailbreak: Exploiting the Reconstruction-Concealment Tradeoff in MLLMs
por: Reza, Md Farhamdur, et al.
Publicado: (2026)

Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
por: Zhou, Richeng, et al.
Publicado: (2026)

Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
por: Jin, Richeng, et al.
Publicado: (2023)

SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding
por: Park, Chanho, et al.
Publicado: (2024)

Hierarchical Federated Learning with SignSGD: A Highly Communication-Efficient Approach
por: Kazemi, Amirreza, et al.
Publicado: (2026)

A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees
por: Jamali, Mohammad Vahid, et al.
Publicado: (2025)

Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles
por: Pervej, Ferdous, et al.
Publicado: (2025)

Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
por: Bars, Batiste Le, et al.
Publicado: (2023)

NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel
por: Thompson, Gabriel, et al.
Publicado: (2024)

When and Why SignSGD Outperforms SGD: A Theoretical Study Based on $\ell_1$-norm Lower Bounds
por: Tao, Hongyi, et al.
Publicado: (2026)

Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
por: Zuo, Shiyuan, et al.
Publicado: (2026)

Federated Learning Nodes Can Reconstruct Peers' Image Data
por: Wilson, Ethan, et al.
Publicado: (2024)

Sign-SGD via Parameter-Free Optimization
por: Medyakov, Daniil, et al.
Publicado: (2025)

Theoretically Guaranteed Distribution Adaptable Learning
por: Xu, Chao, et al.
Publicado: (2024)

CHEEM: Continual Learning by Reuse, New, Adapt and Skip -- A Hierarchical Exploration-Exploitation Approach
por: Savadikar, Chinmay, et al.
Publicado: (2023)

Scaling Laws of SignSGD in Linear Regression: When Does It Outperform SGD?
por: Kim, Jihwan, et al.
Publicado: (2026)

Advancing Hybrid Defense for Byzantine Attacks in Federated Learning
por: Yue, Kai, et al.
Publicado: (2024)

High-Probability Convergence Guarantees of Decentralized SGD
por: Armacki, Aleksandar, et al.
Publicado: (2025)

Phases of Muon: When Muon Eclipses SignSGD
por: Paquette, Elliot, et al.
Publicado: (2026)

From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
por: Xie, Shengping, et al.
Publicado: (2025)

A Hessian-Aware Stochastic Differential Equation for Modelling SGD
por: Li, Xiang, et al.
Publicado: (2024)

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
por: Ertan, Murat Bilgehan, et al.
Publicado: (2026)

Demystifying SGD with Doubly Stochastic Gradients
por: Kim, Kyurae, et al.
Publicado: (2024)

Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD
por: Peng, Ze, et al.
Publicado: (2026)

SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
por: Dahan, Tehila, et al.
Publicado: (2023)

Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization
por: Montasser, Omar, et al.
Publicado: (2024)

Enhancing SignSGD: Small-Batch Convergence Analysis and a Hybrid Switching Strategy
por: Chen, Haoran, et al.
Publicado: (2026)

A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees
por: Azimi-Abarghouyi, Seyed Mohammad, et al.
Publicado: (2025)

H$^3$GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning
por: Xue, Rui, et al.
Publicado: (2025)

Towards Robust Learning to Optimize with Theoretical Guarantees
por: Song, Qingyu, et al.
Publicado: (2025)

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent
por: Surjanovic, Nikola, et al.
Publicado: (2025)