… In this section, we first describe the general notations of federated learning and revisit FedAvg
[1] in Sec 3.1. We then introduce our optimization objective in Sec 3.2, where we will also …

… The superior efficacy of our method is demonstrated on two important medical image
segmentation tasks. Our solution has opened a door in federated learning to enable local client …

… Recently proposed collaborative learning methods such as Federated Learning (FL) …
federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning …

… This paper presents a novel federated learning framework, called Federated Multi-Site
COVID-19 (FEDMSCOV), for efficient, generalizable, and privacy-preserved segmentation of …

… are most interested in the cross-silo federated learning where we have a limited number
of participating clients compared with cross-device federated learning (eg, mobile devices) [17, …

… -time segmentation model is desired. Therefore, the method proposed in this paper adopts
a federated learning-… encoder-decoder architecture for real-time IVUS image segmentation. …

… segmented gossip-based FL algorithm [9], we propose Clustered Hierarchical Distributed
Federated Learning … Secondly, in segmented gossip distributed FL [9], in each round, each …

… Overview of our personalized federated learning framework with local calibration, LC-Fed.
It locally calibrates the features and predictions using the personalized channel selection (…

… Federated learning involves aggregating training results from … the major challenges of
adopting federated learning. … segmentation performance using the federated learning model …

… , a novel federated learning scheme with dual personalization, which improves model
personalization from both feature and prediction aspects to boost image segmentation results. We …