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FedCostWAvg: A new averaging for better federated learning.
Lect. Notes Comput. Sc. 12963 LNCS, 383-391 (2022)
We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community. Our method addresses the problem of how to aggregate multiple models that were trained on different data sets. Conceptually, we propose a new way to choose the weights when averaging the different models, thereby extending the current state of the art (FedAvg). Empirical validation demonstrates that our approach reaches a notable improvement in segmentation performance compared to FedAvg.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Brain Tumor Segmentation ; Federated Learning ; Machine Learning ; Miccai Challenges ; Mri ; Multi-modal Medical Imaging
Language
english
Publication Year
2022
HGF-reported in Year
2022
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Quellenangaben
Volume: 12963 LNCS,
Pages: 383-391
Publisher
Springer
Publishing Place
Berlin [u.a.]
Institute(s)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-505800-001
Scopus ID
85135145620
Erfassungsdatum
2022-11-07