6 citations · 18 across the 10 of their papers we have counts for
10 papers
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation
Minghui Chen, Meirui Jiang, Qi Dou +2
Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratori…
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging
Meirui Jiang, Yuan Zhong, Anjie Le +2
Despite recent progress in enhancing the privacy of federated learning (FL) via differential privacy (DP), the trade-off of DP between privacy protection and performance is still u…
Fair Federated Medical Image Segmentation via Client Contribution Estimation
Meirui Jiang, Holger R Roth, Wenqi Li +6
How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness)…
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification
Tianpeng Deng, Yanqi Huang, Guoqiang Han +7
Histopathological tissue classification is a fundamental task in computational pathology. Deep learning-based models have achieved superior performance but centralized training wit…
On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations
Wenlong Deng, Yuan Zhong, Qi Dou +1
Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with m…
Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer
Junming Chen, Meirui Jiang, Qi Dou +1
Domain generalization (DG) has been a hot topic in image recognition, with a goal to train a general model that can perform well on unseen domains. Recently, federated learning (FL…
Tackling Long-Tailed Category Distribution Under Domain Shifts
Xiao Gu, Yao Guo, Zeju Li +5
Machine learning models fail to perform well on real-world applications when 1) the category distribution P(Y) of the training dataset suffers from long-tailed distribution and 2)…
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients
Nan Lu, Zhao Wang, Xiaoxiao Li +3
Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to lab…
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
Meirui Jiang, Hongzheng Yang, Chen Cheng +1
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data. It is difficult, if possible at all, for such…
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features
Meirui Jiang, Xiaoxiao Li, Xiaofei Zhang +2
How to tackle non-iid data is a crucial topic in federated learning. This challenging problem not only affects training process, but also harms performance of clients not participa…