Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation

Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation

Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive dat...

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Bibliographic Details
Main Authors: Bernardo Camajori Tedeschini, Stefano Savazzi, Roman Stoklasa, Luca Barbieri, Ioannis Stathopoulos, Monica Nicoli, Luigi Serio
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Federated learning
learning over networks
medical imaging
healthcare networks
network architectures
machine learning
Online Access:https://ieeexplore.ieee.org/document/9676574/

Internet

https://ieeexplore.ieee.org/document/9676574/

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