{"total":28,"rows":[{"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":"2303.13137","title":"FedGH: Heterogeneous Federated Learning with Generalized Global Header","created":"2023-03-23","cited_by_count":56,"area":null},{"id":"2206.10206","title":"Personalized Subgraph Federated Learning","created":"2022-06-21","cited_by_count":54,"area":null},{"id":"2309.13546","title":"DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning","created":"2023-09-24","cited_by_count":24,"area":null},{"id":"2406.00488","title":"Federated Model Heterogeneous Matryoshka Representation Learning","created":"2024-06-01","cited_by_count":21,"area":null},{"id":"2205.12493","title":"Federated Self-supervised Learning for Heterogeneous Clients","created":"2022-05-25","cited_by_count":16,"area":null},{"id":"2312.09006","title":"FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning","created":"2023-12-14","cited_by_count":10,"area":null},{"id":"2210.15865","title":"Completely Heterogeneous Federated Learning","created":"2022-10-28","cited_by_count":9,"area":null},{"id":"2311.06879","title":"pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing","created":"2023-11-12","cited_by_count":9,"area":null},{"id":"2305.18497","title":"Collaborative Learning via Prediction Consensus","created":"2023-05-29","cited_by_count":8,"area":null},{"id":"2403.01387","title":"A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications","created":"2024-03-03","cited_by_count":8,"area":null},{"id":"2211.15774","title":"Decentralized Learning with Multi-Headed Distillation","created":"2022-11-28","cited_by_count":5,"area":null},{"id":"2301.11447","title":"Personalised Federated Learning On Heterogeneous Feature Spaces","created":"2023-01-26","cited_by_count":5,"area":null},{"id":"2503.09206","title":"Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients","created":"2025-03-12","cited_by_count":5,"area":null},{"id":"2503.12232","title":"From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification","created":"2025-03-15","cited_by_count":5,"area":null},{"id":"2306.07959","title":"Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings","created":"2023-06-13","cited_by_count":2,"area":null},{"id":"2306.16077","title":"Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization","created":"2023-06-28","cited_by_count":2,"area":null},{"id":"2402.17705","title":"Federated Learning for Estimating Heterogeneous Treatment Effects","created":"2024-02-27","cited_by_count":2,"area":null},{"id":"2602.01626","title":"Toward Enhancing Representation Learning in Federated Multi-Task Settings","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2211.07364","title":"Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning","created":"2022-11-14","cited_by_count":1,"area":null},{"id":"2410.17592","title":"A Kernel Perspective on Distillation-based Collaborative Learning","created":"2024-10-23","cited_by_count":1,"area":null},{"id":"2511.22265","title":"FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2208.09478","title":"Federated Learning of Neural ODE Models with Different Iteration Counts","created":"2022-08-19","cited_by_count":0,"area":null},{"id":"2305.19290","title":"Global Layers: Non-IID Tabular Federated Learning","created":"2023-05-29","cited_by_count":0,"area":null},{"id":"2401.00390","title":"Horizontal Federated Computer Vision","created":"2023-12-31","cited_by_count":0,"area":null},{"id":"2410.11378","title":"Trust-free Personalized Decentralized Learning","created":"2024-10-15","cited_by_count":0,"area":null},{"id":"2602.14244","title":"Federated Ensemble Learning with Progressive Model Personalization","created":"2026-02-15","cited_by_count":0,"area":null},{"id":"2605.11165","title":"COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication","created":"2026-05-11","cited_by_count":0,"area":null}],"next_cursor":null}