Hugging Face Trending Papers August 10, 2026

FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning

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Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence.

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arXiv AI

Aug 11

FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning

arXiv:2608. 09221v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy.

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arXiv Machine Learning

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FedReLa: Imbalanced Federated Learning via Re-Labeling

arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.

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1d ago

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting

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By Hai Anh Tran, Cuong Ta, Truong X. Tran

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arXiv Machine Learning

Aug 4

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

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arXiv AI

Aug 10

FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks

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arXiv AI

Jun 10

Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning

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FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning

FedReLa: Imbalanced Federated Learning via Re-Labeling

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting

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