6 citations · 18 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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)…
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…
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…