{"total":60,"rows":[{"id":"2103.06030","title":"FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space","created":"2021-03-10","cited_by_count":287,"area":null},{"id":"2111.08834","title":"Federated Learning for Smart Healthcare: A Survey","created":"2021-11-16","cited_by_count":184,"area":null},{"id":"2106.06047","title":"Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning","created":"2021-06-10","cited_by_count":111,"area":null},{"id":"2204.10836","title":"Federated Learning Enables Big Data for Rare Cancer Boundary Detection","created":"2022-04-22","cited_by_count":84,"area":null},{"id":"2210.13291","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","created":"2022-10-24","cited_by_count":80,"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":"2112.10775","title":"HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images","created":"2021-12-20","cited_by_count":64,"area":null},{"id":"2105.05874","title":"The Federated Tumor Segmentation (FeTS) Challenge","created":"2021-05-12","cited_by_count":50,"area":null},{"id":"2202.06924","title":"Do Gradient Inversion Attacks Make Federated Learning Unsafe?","created":"2022-02-14","cited_by_count":44,"area":null},{"id":"2106.08600","title":"Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching","created":"2021-06-16","cited_by_count":42,"area":null},{"id":"2112.05752","title":"Specificity-Preserving Federated Learning for MR Image Reconstruction","created":"2021-12-09","cited_by_count":35,"area":null},{"id":"2208.03392","title":"Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions","created":"2022-08-05","cited_by_count":35,"area":null},{"id":"2211.07893","title":"Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges","created":"2022-11-15","cited_by_count":34,"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":"2210.07714","title":"CrowdGuard: Federated Backdoor Detection in Federated Learning","created":"2022-10-14","cited_by_count":27,"area":null},{"id":"2110.00394","title":"Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning","created":"2021-10-01","cited_by_count":25,"area":null},{"id":"2204.08467","title":"IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation","created":"2022-04-16","cited_by_count":22,"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":"2308.13558","title":"Federated Learning for Computer Vision","created":"2023-08-24","cited_by_count":22,"area":null},{"id":"2107.02504","title":"Memory-aware curriculum federated learning for breast cancer classification","created":"2021-07-06","cited_by_count":20,"area":null},{"id":"2104.10195","title":"Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation","created":"2021-04-20","cited_by_count":19,"area":null},{"id":"2107.03901","title":"Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease","created":"2021-07-07","cited_by_count":19,"area":null},{"id":"2108.03437","title":"Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption","created":"2021-08-07","cited_by_count":18,"area":null},{"id":"2203.06338","title":"Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation","created":"2022-03-12","cited_by_count":16,"area":null},{"id":"2206.07156","title":"Federated Multi-organ Segmentation with Inconsistent Labels","created":"2022-06-14","cited_by_count":15,"area":null},{"id":"2206.13079","title":"Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance","created":"2022-06-27","cited_by_count":13,"area":null},{"id":"2405.03636","title":"The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape","created":"2024-05-06","cited_by_count":13,"area":null},{"id":"2103.13511","title":"Addressing catastrophic forgetting for medical domain expansion","created":"2021-03-24","cited_by_count":12,"area":null},{"id":"2304.06662","title":"Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions","created":"2023-04-13","cited_by_count":11,"area":null},{"id":"2110.08378","title":"FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification","created":"2021-10-15","cited_by_count":10,"area":null},{"id":"2304.12012","title":"Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications","created":"2023-04-24","cited_by_count":10,"area":null},{"id":"2106.13208","title":"Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging","created":"2021-06-24","cited_by_count":9,"area":null},{"id":"2205.03168","title":"Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data","created":"2022-05-06","cited_by_count":8,"area":null},{"id":"2211.09317","title":"Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine","created":"2022-11-17","cited_by_count":8,"area":null},{"id":"2408.09227","title":"FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection","created":"2024-08-17","cited_by_count":8,"area":null},{"id":"2111.13230","title":"FedDropoutAvg: Generalizable federated learning for histopathology image classification","created":"2021-11-25","cited_by_count":7,"area":null},{"id":"2309.13016","title":"Understanding Deep Gradient Leakage via Inversion Influence Functions","created":"2023-09-22","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":"2311.16459","title":"On the Effect of Defections in Federated Learning and How to Prevent Them","created":"2023-11-28","cited_by_count":5,"area":null},{"id":"2402.07792","title":"Empowering Federated Learning for Massive Models with NVIDIA FLARE","created":"2024-02-12","cited_by_count":5,"area":null},{"id":"2103.03703","title":"Semi-Supervised Federated Peer Learning for Skin Lesion Classification","created":"2021-03-05","cited_by_count":4,"area":null},{"id":"2109.00202","title":"Federated Learning: Issues in Medical Application","created":"2021-09-01","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":"2209.00170","title":"CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approach","created":"2022-09-01","cited_by_count":2,"area":null},{"id":"2503.09649","title":"Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities","created":"2025-03-12","cited_by_count":2,"area":null},{"id":"2302.08976","title":"Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective","created":"2023-02-17","cited_by_count":1,"area":null},{"id":"2303.06783","title":"Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging","created":"2023-03-12","cited_by_count":1,"area":null},{"id":"2405.13832","title":"Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review","created":"2024-05-22","cited_by_count":1,"area":null},{"id":"2504.20656","title":"Federated learning, ethics, and the double black box problem in medical AI","created":"2025-04-29","cited_by_count":1,"area":null},{"id":"2506.17805","title":"AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator","created":"2025-06-21","cited_by_count":1,"area":null},{"id":"2510.19118","title":"A Novel Approach to Breast Cancer Segmentation using U-Net Model with Attention Mechanisms and FedProx","created":"2025-10-21","cited_by_count":1,"area":null},{"id":"2112.01629","title":"Engineering AI Tools for Systematic and Scalable Quality Assessment in Magnetic Resonance Imaging","created":"2021-12-02","cited_by_count":0,"area":null},{"id":"2202.08238","title":"A multi-reconstruction study of breast density estimation using Deep Learning","created":"2022-02-16","cited_by_count":0,"area":null},{"id":"2308.02454","title":"SoK: Assessing the State of Applied Federated Machine Learning","created":"2023-08-03","cited_by_count":0,"area":null},{"id":"2310.09444","title":"Tackling Heterogeneity in Medical Federated learning via Vision Transformers","created":"2023-10-13","cited_by_count":0,"area":null},{"id":"2403.12044","title":"Mobile Application for Oral Disease Detection using Federated Learning","created":"2023-10-27","cited_by_count":0,"area":null},{"id":"2405.14900","title":"Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge","created":"2024-05-22","cited_by_count":0,"area":null},{"id":"2505.12815","title":"Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2508.15452","title":"DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2605.09137","title":"Evaluating Federated Learning approaches for mammography under breast density heterogeneity","created":"2026-05-09","cited_by_count":0,"area":null}],"next_cursor":null}