arXiv Machine Learning June 24, 2026 By Zesen Wang, Mikael Johansson

Decentralized SGD with Controlled Disagreement Finds Flatter Minima

Read the original on arXiv Machine Learning →

arXiv:2602. 02899v2 Announce Type: replace Abstract: Decentralized training is often regarded as inferior to centralized training because the consensus errors between workers are thought to undermine convergence and generalization.

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arXiv Machine Learning

Jun 9

Improved Convergence Analysis of Topology Dependence in Decentralized SGD

arXiv:2606. 09154v1 Announce Type: new Abstract: Decentralized SGD is a fundamental algorithm in decentralized learning, although the influence of an underlying network topology on its convergence behavior is not yet fully understood.

By Yuki Takezawa, Anastasia Koloskova, Sebastian U. Stich

arXiv Machine Learning

Aug 11

Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum

arXiv:2504. 12742v2 Announce Type: replace Abstract: Decentralized Federated Learning (DFL) enables collaborative model training without relying on a central server.

By Yuan Zhou, Xinli Shi, Xuelong Li, Jiachen Zhong, Guanghui Wen, Jinde Cao

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arXiv Machine Learning

Jul 28

Distributed Convolutional Rank Regression over Decentralized Networks

arXiv:2607. 23639v1 Announce Type: cross Abstract: This paper studies convolution rank regression (CRR) over decentralized distributed learning networks.

By Chunjing Li, Tiange Zhao, Xiaohui Yuan

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arXiv Machine Learning

Jun 16

Communication-Efficient Distributed Training for Collaborative Flat Optima Recovery in Deep Learning

arXiv:2507. 20424v3 Announce Type: replace Abstract: We study centralized distributed data parallel training of deep neural networks (DNNs), aiming to improve the trade-off between communication efficiency and model performance of the local gradient methods.

By Tolga Dimlioglu, Anna Choromanska

arXiv Machine Learning

Sep 10

Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration

arXiv:2609.07312v1 Announce Type: new Abstract: This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attack...

By Xiao Ma, Hong Shen, Hui Tian, Wenqi Lyu, Wei Ke

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arXiv AI

Aug 11

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning

arXiv:2608. 09208v1 Announce Type: cross Abstract: Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL).

By Van Truong Vo, Khoa Nguyen, Taehong Kim

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