arXiv Machine Learning July 3, 2026 By Haemin Park, Diego Klabjan, Martin W. Braun, Xiuqi Li, Balakrishnan Ananthanarayanan

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

Read the original on arXiv Machine 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.

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

arXiv Machine Learning

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

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.

By Yutong He, Zhengyang Huang, Jiahe Geng, Kun Yuan

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The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient

arXiv:2602. 11557v2 Announce Type: replace Abstract: A variety of widely used optimization methods like SignSGD and Muon can be interpreted as instances of steepest descent under different norm-induced geometries.

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Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning

arXiv:2607. 00275v1 Announce Type: cross Abstract: Federated Learning (FL) is a distributed machine learning (ML) paradigm with collaboration among multiple clients without sharing data.

By Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas Lundell

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Jun 10

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

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

arXiv Machine Learning

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PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning

arXiv:2502. 08829v2 Announce Type: replace Abstract: Federated learning (FL) with non-IID data often degrades client performance below local training baselines.

By Ahmed Elhussein, Florent Pollet, Gamze G\"ursoy

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arXiv AI

Jun 30

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning

arXiv:2606. 28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm.

By Wenhao Yuan, Chenchen Lin, Jian Chen, Jinfeng Xu, Zewei Liu, Edith Cheuk Han Ngai

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

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