Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling

Autor: Kumar, Sourav, Lakshminarayanan, A., Chang, Ken, Guretno, Feri, Mien, Ivan Ho, Kalpathy-Cramer, Jayashree, Krishnaswamy, Pavitra, Singh, Praveer

Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not contribute equally, some contribute more data, some better quality dat

Externí odkaz: http://arxiv.org/abs/2209.05424

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Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling.

Autor: Kumar S; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA., Lakshminarayanan A; Institute for Infocomm Research, ASTAR, Singapore., Chang K; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA., Guretno F; Institute for Infocomm Research, ASTAR, Singapore., Mien IH; Institute for Infocomm Research, ASTAR, Singapore., Kalpathy-Cramer J; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA., Krishnaswamy P; Institute for Infocomm Research, ASTAR, Singapore., Singh P; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.

Publikováno v: Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : Third MICCAI Workshop, DeCaF 2022 and Second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Sin.. [Distrib Collab Fed Learn Afford AI Healthc Resour Div Glob Health (2022)] 2022 Sep; Vol. 13573, pp. 119-129. Date of Electronic Publication: 2022 Oct 07.