arXiv Machine Learning
Jun 15
Federated Learning for Feature Generalization with Convex Constraints
arXiv:2606. 14416v1 Announce Type: new Abstract: Federated learning (FL) often struggles with generalization due to heterogeneous client data.
FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations
arXiv:2608. 09687v1 Announce Type: new Abstract: Federated learning (FL) in Low Earth Orbit (LEO) satellite constellations is affected by non-IID data and irregular ground-station visibility, both driven by orbital geometry.
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arXiv AI
1d ago
FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting
arXiv:2608. 14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy.
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 Machine Learning
Aug 5
FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations
arXiv:2608. 03436v1 Announce Type: cross Abstract: Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or synchronized training inefficient and hard to scale.
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.
arXiv Machine Learning
Jul 7
FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering
arXiv:2607. 04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization, and sharp minima compared to centralized training.
arXiv AI
Jun 10
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
arXiv:2606. 10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades 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.
arXiv AI
Jul 10
FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.
arXiv AI
Aug 10
FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks
arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.
arXiv Machine Learning
Jun 15
FedSPC: Shared Parameter Correction for Personalized Federated Learning
arXiv:2606. 13748v1 Announce Type: new Abstract: Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation.
arXiv Machine Learning
Jun 16
Distribution Alignment for One-Shot Federated Learning via Optimal Transport
arXiv:2606. 16655v1 Announce Type: new Abstract: One-Shot Federated Learning (OSFL) addresses extreme communication regimes in which clients interact with the server only once, amplifying the impact of heterogeneous client data distributions.