… In this paper, we propose a new federated learning method for semantic segmentation,
FedSeg, to address the above issues. A standard objective function for semantic segmentation is …

… It is of great challenges for conventional federated learning approaches to efficiently utilize
… federated learning to tackle this problem. In particular, we propose a segmented gossip …

… -agnostic unified federated learning framework, named FedMix, for medical image segmentation
based on mixed image labels. In FedMix, each client updates the federated model by …

… data and diversity of their LANs, a segmented federated learning is proposed in this research,
… consists of two main parts: intrusion detection in LANs and segmented federated learning. …

… In this article, we propose a novel adaptive learning scheme of SegmentedFederated
Learning (Segmented-FL) (Fig. 1). We employ periodic local model evaluation and segmentation …

… -time platform to support network and federated learning functions integration, validating the
… , focusing in particular on brain tumor segmentation. To demonstrate the system in a real …

… We also proposed a survey of federated learning methodologies to enhance global segmentation
performance … This paper focuses on the issues of federated learning in segmentation. …

… learning approach of Segmented-Federated Learning (SegmentedFL) for better learning from
… We employ periodic local model evaluation and segmentation for adaptive model training. …

… , we adopt federated learning to train an efficient network of medical image segmentation by
… This paper applies federated learning to brain tumor segmentation and propose an efficient …

… FL algorithms learn a shared model leveraging a distributed … Hence, the usage of FL for
Semantic Segmentation in … the first Semantic Segmentation benchmark in a Federated Learning …