{"total":35,"rows":[{"id":"2203.11751","title":"FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction","created":"2022-03-22","cited_by_count":181,"area":null},{"id":"2306.05980","title":"Federated Learning for Medical Image Analysis: A Survey","created":"2023-06-09","cited_by_count":78,"area":null},{"id":"2303.16181","title":"Learning Federated Visual Prompt in Null Space for MRI Reconstruction","created":"2023-03-28","cited_by_count":40,"area":null},{"id":"2202.04175","title":"Federated Learning of Generative Image Priors for MRI Reconstruction","created":"2022-02-08","cited_by_count":33,"area":null},{"id":"2306.01334","title":"Federated Domain Generalization: A Survey","created":"2023-06-02","cited_by_count":25,"area":null},{"id":"2207.06509","title":"One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis","created":"2022-07-13","cited_by_count":22,"area":null},{"id":"2206.03709","title":"Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging","created":"2022-06-08","cited_by_count":14,"area":null},{"id":"2310.09650","title":"Multimodal Federated Learning in Healthcare: a Review","created":"2023-10-14","cited_by_count":13,"area":null},{"id":"2204.02450","title":"Federated Cross Learning for Medical Image Segmentation","created":"2022-04-05","cited_by_count":9,"area":null},{"id":"2306.02673","title":"Cross-Modal Vertical Federated Learning for MRI Reconstruction","created":"2023-06-05","cited_by_count":9,"area":null},{"id":"2308.06051","title":"Towards Instance-adaptive Inference for Federated Learning","created":"2023-08-11","cited_by_count":7,"area":null},{"id":"2401.03609","title":"Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities","created":"2024-01-07","cited_by_count":6,"area":null},{"id":"2503.00908","title":"Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model","created":"2025-03-02","cited_by_count":6,"area":null},{"id":"2305.02774","title":"Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction","created":"2023-05-04","cited_by_count":5,"area":null},{"id":"2308.10910","title":"Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction","created":"2023-08-20","cited_by_count":5,"area":null},{"id":"2502.04521","title":"Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction","created":"2025-02-06","cited_by_count":5,"area":null},{"id":"2308.10162","title":"Rethinking Client Drift in Federated Learning: A Logit Perspective","created":"2023-08-20","cited_by_count":4,"area":null},{"id":"2204.13808","title":"Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning","created":"2022-04-25","cited_by_count":3,"area":null},{"id":"2304.00570","title":"FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising","created":"2023-04-02","cited_by_count":3,"area":null},{"id":"2312.02015","title":"ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy","created":"2023-12-04","cited_by_count":2,"area":null},{"id":"2410.19765","title":"A New Perspective to Boost Performance Fairness for Medical Federated Learning","created":"2024-10-12","cited_by_count":2,"area":null},{"id":"2501.15949","title":"Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data","created":"2025-01-27","cited_by_count":2,"area":null},{"id":"2502.00319","title":"Physics-Inspired Distributed Radio Map Estimation","created":"2025-02-01","cited_by_count":2,"area":null},{"id":"2508.20414","title":"Federated Learning for Large Models in Medical Imaging: A Comprehensive Review","created":"2025-08-28","cited_by_count":2,"area":null},{"id":"2301.12798","title":"Reliable Federated Disentangling Network for Non-IID Domain Feature","created":"2023-01-30","cited_by_count":1,"area":null},{"id":"2308.13970","title":"FAM: fast adaptive federated meta-learning","created":"2023-08-26","cited_by_count":1,"area":null},{"id":"2608.10144","title":"SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks","created":"2026-08-10","cited_by_count":1,"area":null},{"id":"2407.17780","title":"HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging","created":"2024-07-25","cited_by_count":0,"area":null},{"id":"2412.11463","title":"FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning","created":"2024-12-16","cited_by_count":0,"area":null},{"id":"2501.14158","title":"Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration","created":"2025-01-24","cited_by_count":0,"area":null},{"id":"2503.13745","title":"FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2503.15390","title":"FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation","created":"2025-03-19","cited_by_count":0,"area":null},{"id":"2507.00230","title":"PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction","created":"2025-06-30","cited_by_count":0,"area":null},{"id":"2508.10299","title":"Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2603.13422","title":"Projection Guided Personalized Federated Learning for Low Dose CT Denoising","created":"2026-03-12","cited_by_count":0,"area":null}],"next_cursor":null}