Exploring investment strategies for federated learning infrastructure in medical care
Xing, J; Zhang, X; Jiang, Z; et al.Zhang, R; Zha, C; Yin, H
Date: 8 March 2022
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
Recently, federated learning has gained substantial
attention in medical care where privacy-preserving cooperation
among hospitals is required. However, in a real-world situation, the deployment of a federated learning system among
hospitals requires heavy investment in computing and network
infrastructure. Under such a case, making ...
Recently, federated learning has gained substantial
attention in medical care where privacy-preserving cooperation
among hospitals is required. However, in a real-world situation, the deployment of a federated learning system among
hospitals requires heavy investment in computing and network
infrastructure. Under such a case, making investment effective
across computing power and network capability is essential. In
this paper, we propose an investment methodology following the
growth saturation of learning efficiency. We also systematically
study the impacts of non-investment factors on the application
of this methodology. With consideration of relevant cost models,
the methodology is validated cost-effective.
Computer Science
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0