{"total":58,"rows":[{"id":"2106.06843","title":"Federated Learning on Non-IID Data: A Survey","created":"2021-06-12","cited_by_count":346,"area":null},{"id":"2307.10616","title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","created":"2023-07-20","cited_by_count":161,"area":null},{"id":"2111.14213","title":"Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning","created":"2021-11-28","cited_by_count":114,"area":null},{"id":"2107.00233","title":"FedMix: Approximation of Mixup under Mean Augmented Federated Learning","created":"2021-07-01","cited_by_count":108,"area":null},{"id":"2311.06750","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","created":"2023-11-12","cited_by_count":99,"area":null},{"id":"2310.05077","title":"FedFed: Feature Distillation against Data Heterogeneity in Federated Learning","created":"2023-10-08","cited_by_count":54,"area":"distill"},{"id":"2206.02465","title":"Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning","created":"2022-06-06","cited_by_count":52,"area":null},{"id":"2209.04599","title":"Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation","created":"2022-09-10","cited_by_count":45,"area":"distill"},{"id":"2205.13462","title":"FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction","created":"2022-05-26","cited_by_count":35,"area":null},{"id":"2212.10888","title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","created":"2022-12-21","cited_by_count":31,"area":null},{"id":"2106.07085","title":"Survey: Image Mixing and Deleting for Data Augmentation","created":"2021-06-13","cited_by_count":26,"area":null},{"id":"2404.18848","title":"FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition","created":"2024-04-29","cited_by_count":26,"area":null},{"id":"2206.05507","title":"Federated Learning with GAN-based Data Synthesis for Non-IID Clients","created":"2022-06-11","cited_by_count":25,"area":null},{"id":"2306.01334","title":"Federated Domain Generalization: A Survey","created":"2023-06-02","cited_by_count":25,"area":null},{"id":"2211.10878","title":"DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics","created":"2022-11-20","cited_by_count":22,"area":null},{"id":"2310.00339","title":"FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation","created":"2023-09-30","cited_by_count":22,"area":null},{"id":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"area":null},{"id":"2204.12493","title":"One-shot Federated Learning without Server-side Training","created":"2022-04-26","cited_by_count":20,"area":null},{"id":"2409.05202","title":"A Survey on Mixup Augmentations and Beyond","created":"2024-09-08","cited_by_count":20,"area":null},{"id":"2410.04810","title":"FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models","created":"2024-10-07","cited_by_count":16,"area":"diffusion"},{"id":"2402.07011","title":"FedImpro: Measuring and Improving Client Update in Federated Learning","created":"2024-02-10","cited_by_count":15,"area":null},{"id":"2403.12329","title":"FedFisher: Leveraging Fisher Information for One-Shot Federated Learning","created":"2024-03-19","cited_by_count":15,"area":null},{"id":"2308.04761","title":"Feature Matching Data Synthesis for Non-IID Federated Learning","created":"2023-08-09","cited_by_count":13,"area":null},{"id":"2411.07182","title":"Revisiting Ensembling in One-Shot Federated Learning","created":"2024-11-11","cited_by_count":13,"area":null},{"id":"2503.06457","title":"Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning","created":"2025-03-09","cited_by_count":12,"area":null},{"id":"2104.00365","title":"Federated Few-Shot Learning with Adversarial Learning","created":"2021-04-01","cited_by_count":11,"area":null},{"id":"2405.07925","title":"Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data","created":"2024-05-13","cited_by_count":11,"area":"diffusion"},{"id":"2410.20380","title":"FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion","created":"2024-10-27","cited_by_count":11,"area":null},{"id":"2305.06124","title":"FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment","created":"2023-05-10","cited_by_count":9,"area":null},{"id":"2206.03420","title":"An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning","created":"2022-06-07","cited_by_count":7,"area":null},{"id":"2210.08464","title":"Federated Learning with Privacy-Preserving Ensemble Attention Distillation","created":"2022-10-16","cited_by_count":6,"area":null},{"id":"2502.09104","title":"One-shot Federated Learning Methods: A Practical Guide","created":"2025-02-13","cited_by_count":6,"area":null},{"id":"2503.03995","title":"Subgraph Federated Learning for Local Generalization","created":"2025-03-06","cited_by_count":6,"area":null},{"id":"2205.02359","title":"FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation","created":"2022-05-04","cited_by_count":5,"area":null},{"id":"2205.09305","title":"FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data","created":"2022-05-19","cited_by_count":4,"area":null},{"id":"2207.04103","title":"StatMix: Data augmentation method that relies on image statistics in federated learning","created":"2022-07-08","cited_by_count":4,"area":null},{"id":"2503.07505","title":"From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges","created":"2025-03-10","cited_by_count":4,"area":null},{"id":"2211.05554","title":"Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization","created":"2022-11-10","cited_by_count":3,"area":null},{"id":"2410.21119","title":"A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning","created":"2024-10-28","cited_by_count":3,"area":null},{"id":"2501.11002","title":"pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup","created":"2025-01-19","cited_by_count":3,"area":null},{"id":"2310.15371","title":"Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation","created":"2023-10-23","cited_by_count":2,"area":null},{"id":"2311.17750","title":"Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning","created":"2023-11-29","cited_by_count":2,"area":null},{"id":"2409.07734","title":"DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning","created":"2024-09-12","cited_by_count":2,"area":null},{"id":"2502.16832","title":"FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning","created":"2025-02-24","cited_by_count":2,"area":null},{"id":"2505.02426","title":"Towards One-shot Federated Learning: Advances, Challenges, and Future Directions","created":"2025-05-05","cited_by_count":2,"area":null},{"id":"2403.11743","title":"PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks","created":"2024-03-18","cited_by_count":1,"area":null},{"id":"2409.13235","title":"Balancing Label Imbalance in Federated Environments Using Only Mixup and Artificially-Labeled Noise","created":"2024-09-20","cited_by_count":1,"area":null},{"id":"2412.12844","title":"Concurrent vertical and horizontal federated learning with fuzzy cognitive maps","created":"2024-12-17","cited_by_count":1,"area":null},{"id":"2511.16423","title":"TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models","created":"2025-11-20","cited_by_count":1,"area":"mllm"},{"id":"2210.00737","title":"Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests","created":"2022-10-03","cited_by_count":0,"area":null},{"id":"2303.02445","title":"Federated Semi-Supervised Learning with Annotation Heterogeneity","created":"2023-03-04","cited_by_count":0,"area":null},{"id":"2304.04147","title":"FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybrid","created":"2023-04-09","cited_by_count":0,"area":null},{"id":"2405.16233","title":"Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing","created":"2024-05-25","cited_by_count":0,"area":null},{"id":"2406.00150","title":"Non-Federated Multi-Task Split Learning for Heterogeneous Sources","created":"2024-05-31","cited_by_count":0,"area":null},{"id":"2409.07351","title":"Federated Impression for Learning with Distributed Heterogeneous Data","created":"2024-09-11","cited_by_count":0,"area":null},{"id":"2510.08058","title":"FedDTRE: Federated Dialogue Generation Models Powered by Trustworthiness Evaluation","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2607.08368","title":"FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2608.02222","title":"CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning","created":"2026-08-03","cited_by_count":0,"area":null}],"next_cursor":null}