Hugging Face Trending Papers
FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning
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arXiv AI
Sep 10
FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning
arXiv:2609.07192v1 Announce Type: cross Abstract: Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather...
Hugging Face Trending Papers
Aug 10
FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning
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.
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.
Hugging Face Trending Papers
Aug 24
FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning
Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillat...
arXiv AI
Aug 25
FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning
FedCC is a new algorithm for distillation-based federated learning that tackles label distribution skew by allowing clients to mark ambiguous samples as 'unknown' instead of forcing a potentially wrong classification. By adding this extra class and calibrating pseudo-labels on a public dataset, FedCC balances confidence across majority and minority classes. Experiments show that FedCC outperforms existing methods, achieving 67.3% accuracy even when each client has data from only one of ten classes, whereas baselines drop to near-random performance.
arXiv Machine Learning
Jun 25
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.