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Mächler, L.* ; Ezhov, I.* ; Shit, S.* ; Paetzold, J.C.

FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated Learning.

In:. Berlin [u.a.]: Springer, 2023. 209-217 (Lect. Notes Comput. Sc. ; 14092 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
This paper presents FedPIDAvg, the winning submission to the Federated Tumor Segmentation Challenge 2022 (FETS22). Inspired by FedCostWAvg, our winning contribution to FETS21, we contribute an improved aggregation strategy for federated and collaborative learning. FedCostWAvg is a weighted averaging method that not only considers the number of training samples of each cluster but also the size of the drop of the respective cost function in the last federated round. This can be interpreted as the derivative part of a PID controller (proportional-integral-derivative controller). In FedPIDAvg, we further add the missing integral term. Another key challenge was the vastly varying size of data samples per center. We addressed this by modeling the data center sizes as following a Poisson distribution and choosing the training iterations per center accordingly. Our method outperformed all other submissions.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Brain Tumor Segmentation ; Control ; Federated Learning ; Machine Learning ; Miccai Challenges ; Mri ; Multi-modal Medical Imaging
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14092 LNCS, Heft: , Seiten: 209-217 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505800-001
Förderungen Graduate School of Bioengineering, Technical University of Munich
International Graduate School of Science and Engineering (IGSSE)
Technical University of Munich -Institute for Advanced Study - German Excellence Initiative
Translational Brain Imaging Training Network (TRABIT) under the European Union's 'Horizon 2020' research & innovation program
DCoMEX project - Federal Ministry of Education and Research of Germany
'Ecole normale superieure in Paris
DOI 10.1007/978-3-031-44153-0_20
WOS ID 001206018200020
Scopus ID 85185721597
Erfassungsdatum 2024-03-08
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