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Mächler, L.* ; Ezhov, I.* ; Kofler, F.* ; Shit, S.* ; Paetzold, J.C. ; Loehr, T.* ; Zimmer, C.* ; Wiestler, B.* ; Menze, B.H.*

FedCostWAvg: A new averaging for better federated learning.

Lect. Notes Comput. Sc. 12963 LNCS, 383-391 (2022)
Postprint DOI
Open Access Green
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
Corresponding Author
Keywords Brain Tumor Segmentation ; Federated Learning ; Machine Learning ; Miccai Challenges ; Mri ; Multi-modal Medical Imaging
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 12963 LNCS, Issue: , Pages: 383-391 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute for Tissue Engineering and Regenerative Medicine (ITERM)