Kumar, S., Lakshminarayanan, A., Chang, K., Guretno, F., Mien, I. H., Kalpathy-Cramer, J., Krishnaswamy, P., & Singh, P. (2022). Towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling. Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health, 119–129. https://doi.org/10.1007/978-3-031-18523-6_12
Abstract:
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 data or some more
diverse data. To fairly rank the contribution of different institutions,
Shapley value (SV) has emerged as the method of choice. Exact SV computation
is impossibly expensive, especially when there are hundreds of
contributors. Existing SV computation techniques use approximations.
However, in healthcare where the number of contributing institutions are
likely not of a colossal scale, computing exact SVs is still exorbitantly expensive,
but not impossible. For such settings, we propose an efficient SV
computation technique called SaFE (Shapley Value for Federated Learning
using Ensembling). We empirically show that SaFE computes values
that are close to exact SVs, and that it performs better than current SV
approximations. This is particularly relevant in medical imaging setting
where widespread heterogeneity across institutions is rampant and fast
accurate data valuation is required to determine the contribution of each
participant in multi-institutional collaborative learning.
License type:
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Funding Info:
This research / project is supported by the National Science Foundation (NSF) - Assistive Integrative Support Tool for Retinopathy of Prematurity
Grant Reference no. : NSF1622542
This research / project is supported by the National Institutes of Health (NIH) - Quantitative MRI of Glioblastoma Response
Grant Reference no. : U01CA154601
This research / project is supported by the National Institutes of Health (NIH) - Informatics Tools for Optimized Imaging Biomarkers for Cancer Research & Discovery
Grant Reference no. : U24CA180927
This research / project is supported by the National Institutes of Health (NIH) - Quantitative Image Informatics for Cancer Research (QIICR)
Grant Reference no. : U24CA180918
Description:
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-18523-6_12