{"total":18,"rows":[{"id":"2308.12305","title":"FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning","created":"2023-08-21","cited_by_count":60,"area":null},{"id":"2402.15070","title":"Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting","created":"2024-02-23","cited_by_count":27,"area":null},{"id":"2306.16064","title":"Federated Generative Learning with Foundation Models","created":"2023-06-28","cited_by_count":23,"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":"2404.12130","title":"One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity","created":"2024-04-18","cited_by_count":8,"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":"2504.05238","title":"Federated Learning for Medical Image Classification: A Comprehensive Benchmark","created":"2025-04-07","cited_by_count":6,"area":null},{"id":"2307.02140","title":"Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives","created":"2023-07-05","cited_by_count":3,"area":null},{"id":"2412.04650","title":"Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Models","created":"2024-12-05","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":"2507.10430","title":"Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout","created":"2025-07-14","cited_by_count":2,"area":null},{"id":"2509.22700","title":"Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning","created":"2025-09-21","cited_by_count":2,"area":null},{"id":"2510.18837","title":"FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning","created":"2025-10-21","cited_by_count":1,"area":null},{"id":"2606.16655","title":"Distribution Alignment for One-Shot Federated Learning via Optimal Transport","created":"2026-06-15","cited_by_count":1,"area":null},{"id":"2407.05098","title":"FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning","created":"2024-07-06","cited_by_count":0,"area":null},{"id":"2508.09174","title":"FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective","created":"2025-08-07","cited_by_count":0,"area":null},{"id":"2602.21078","title":"ProxyFL: A Proxy-Guided Framework for Federated Semi-Supervised Learning","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2605.10748","title":"Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs","created":"2026-05-11","cited_by_count":0,"area":null}],"next_cursor":null}