arXiv Machine Learning June 10, 2026 By Yutong He, Zhengyang Huang, Jiahe Geng, Kun Yuan

FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection

Read the original on arXiv Machine Learning →

arXiv:2604. 24012v3 Announce Type: replace Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource-constrained environments.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

benchmarks

arXiv Machine Learning

Aug 10

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.

By Grigory Malinovsky

fine-tuning

arXiv Machine Learning

Jun 16

Communication-Efficient Neural Tangent Kernels for Heterogeneous Decentralized Federated Learning

arXiv:2512. 12737v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) enables collaborative model training without a central server, but converges slowly under statistical heterogeneity.

By Li Xia

data-engineering

arXiv Machine Learning

Jul 3

Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning

arXiv:2607. 01474v1 Announce Type: new Abstract: Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot be addressed by standard centralized techniques due to privacy and heterogeneity constraints.

By Haemin Park, Diego Klabjan, Martin W. Braun, Xiuqi Li, Balakrishnan Ananthanarayanan

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 2

Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee

arXiv:2605. 28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks.

By Shiyuan Zuo, Jiashuo Li, Rongfei Fan, Han Hu, Jie Xu

rag benchmarks

arXiv AI

Jun 10

FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching

arXiv:2606. 10124v1 Announce Type: cross Abstract: Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds.

By Haoran Zhang, Cain\~a Figueiredo Pereira, Marie Siew, Xutong Liu, Carlee Joe-Wong, Rachid El-Azouzi

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Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum

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