… Purpose: To investigate the efficacy of federated learning (FL) compared to industry-level
centralized learning (CL) for segmenting acute infarct and white matter hyperintensity. …

… learning & federated learning to the readers. Then, I describe the types of federated learning
systems and the popular Federated … of SegViz: The federated learning framework for …

Federated learning (FL) effectively mitigates the data silo challenge brought about by policies
and privacy concerns, implicitly harnessing more data for deep model training. However, …

… S D L is the segmentation of a deep learning model and S m is the manual segmentation. The
… HR, ZX, JT, DX, and MGF contributed to the underlying federated learning framework and …

… Federated learning has emerged as an important distributed learning paradigm, which …
In this work, we propose FCCL (Federated Cross-Correlation and Continual Learning). For …

… -institutional federated learning for brain tumour segmentation … —Brain tumour segmentation,
3D U-Net, Federated learning … federated learning techniques to brain tumour segmentation. …

… Shingi et al. proposed to apply segmented federated learning (Segmented-FL) to construct
a more efficient intrusion detection system [35]. A key difference of the proposed Segmented-…

… The paper combs through recent papers on federated learning and medical industry …
models of federated learning, researches the application of federated learning in healthcare, and …

… Lung segmentation helps doctors in analyzing and diagnosing … Federated Learning (FL)
framework, using transfer learning … FL with Transfer learning doesn't need the parallel training of …

… review of federated learning in … segmentation tasks. Finally, we summarized in Table 1 the
strengths and weaknesses of machine learning algorithms performing on federated learning…