Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current literature on image registration is generally based on the assumption that images are usually accessible to the researcher, from which the spatial transformation is subsequently estimated. This...
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September 18, 2022 (v1)Conference paperUploaded on: December 3, 2022
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July 30, 2024 (v1)Conference paper
Secure Aggregation (SA) stands as a crucial component in modern Federated Learning (FL) systems, facilitating collaborative training of a global machine learning model while protecting the privacy of individual clients' local datasets. Many existing SA protocols described in the FL literature operate synchronously, leading to no-table runtime...
Uploaded on: August 10, 2024 -
July 30, 2024 (v1)Conference paper
Secure Aggregation (SA) stands as a crucial component in modern Federated Learning (FL) systems, facilitating collaborative training of a global machine learning model while protecting the privacy of individual clients' local datasets. Many existing SA protocols described in the FL literature operate synchronously, leading to notable runtime...
Uploaded on: October 25, 2024 -
May 2024 (v1)Journal article
Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently...
Uploaded on: March 14, 2024 -
April 25, 2023 (v1)Publication
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security. While today several FL libraries are proposed to data scientists and users, most of these frameworks are not...
Uploaded on: April 29, 2023