{"total":29,"rows":[{"id":"2306.05879","title":"FedWon: Triumphing Multi-domain Federated Learning Without Normalization","created":"2023-06-09","cited_by_count":11,"area":null},{"id":"2401.09656","title":"Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks","created":"2024-01-18","cited_by_count":11,"area":null},{"id":"2403.02619","title":"Training Machine Learning models at the Edge: A Survey","created":"2024-03-05","cited_by_count":11,"area":null},{"id":"2303.06530","title":"Making Batch Normalization Great in Federated Deep Learning","created":"2023-03-12","cited_by_count":8,"area":null},{"id":"2409.19560","title":"Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving","created":"2024-09-29","cited_by_count":8,"area":null},{"id":"2302.13562","title":"Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence","created":"2023-02-27","cited_by_count":5,"area":null},{"id":"2410.17265","title":"Federated brain tumor segmentation: an extensive benchmark","created":"2024-10-07","cited_by_count":5,"area":null},{"id":"2503.17070","title":"A Thorough Assessment of the Non-IID Data Impact in Federated Learning","created":"2025-03-21","cited_by_count":5,"area":null},{"id":"2405.14670","title":"Overcoming the Challenges of Batch Normalization in Federated Learning","created":"2024-05-23","cited_by_count":4,"area":null},{"id":"2410.13602","title":"Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach","created":"2024-10-17","cited_by_count":4,"area":null},{"id":"2509.05213","title":"An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data","created":"2025-09-05","cited_by_count":4,"area":null},{"id":"2405.20126","title":"Federated and Transfer Learning for Cancer Detection Based on Image Analysis","created":"2024-05-30","cited_by_count":3,"area":null},{"id":"2502.03092","title":"E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing","created":"2025-02-05","cited_by_count":3,"area":null},{"id":"2308.09565","title":"Understanding the Role of Layer Normalization in Label-Skewed Federated Learning","created":"2023-08-18","cited_by_count":2,"area":null},{"id":"2310.19558","title":"Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification","created":"2023-10-30","cited_by_count":2,"area":null},{"id":"2501.11267","title":"Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks","created":"2025-01-20","cited_by_count":2,"area":null},{"id":"2506.07581","title":"FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2310.07171","title":"Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients","created":"2023-10-11","cited_by_count":1,"area":null},{"id":"2410.03499","title":"FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator","created":"2024-10-04","cited_by_count":1,"area":null},{"id":"2509.13219","title":"On the Out-of-Distribution Backdoor Attack for Federated Learning","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2303.06180","title":"Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels","created":"2023-03-10","cited_by_count":0,"area":null},{"id":"2406.02600","title":"Data Quality in Edge Machine Learning: A State-of-the-Art Survey","created":"2024-06-01","cited_by_count":0,"area":null},{"id":"2409.15898","title":"FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning","created":"2024-09-24","cited_by_count":0,"area":null},{"id":"2410.02006","title":"An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration","created":"2024-10-02","cited_by_count":0,"area":null},{"id":"2502.17260","title":"Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach","created":"2025-02-24","cited_by_count":0,"area":null},{"id":"2504.13462","title":"Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling","created":"2025-04-18","cited_by_count":0,"area":null},{"id":"2505.21877","title":"Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning","created":"2025-05-28","cited_by_count":0,"area":null},{"id":"2510.07126","title":"Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2606.14416","title":"Federated Learning for Feature Generalization with Convex Constraints","created":"2026-06-12","cited_by_count":0,"area":null}],"next_cursor":null}