@inproceedings{6e6cafed7ad24b32b11057e03ceb0f1e,
title = "FedCostWAvg: A New Averaging for Better Federated Learning",
abstract = "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.",
keywords = "Brain tumor segmentation, Federated learning, MICCAI challenges, MRI, Machine learning, Multi-modal medical imaging",
author = "Leon M{\"a}chler and Ivan Ezhov and Florian Kofler and Suprosanna Shit and Paetzold, {Johannes C.} and Timo Loehr and Claus Zimmer and Benedikt Wiestler and Menze, {Bjoern H.}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
year = "2022",
doi = "10.1007/978-3-031-09002-8_34",
language = "English",
isbn = "9783031090011",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "383--391",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion",
}