dc.contributor.author | Xing, J | |
dc.contributor.author | Zhang, X | |
dc.contributor.author | Jiang, Z | |
dc.contributor.author | Zhang, R | |
dc.contributor.author | Zha, C | |
dc.contributor.author | Yin, H | |
dc.date.accessioned | 2021-07-26T06:53:46Z | |
dc.date.issued | 2022-03-08 | |
dc.description.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 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. | en_GB |
dc.description.sponsorship | National Key Research and Development Program of China | en_GB |
dc.description.sponsorship | National Natural Science Foundation of China | en_GB |
dc.description.sponsorship | Hunan Provincial Research and Development Program in Key Areas | en_GB |
dc.description.sponsorship | Natural Science Foundation of Jiangsu | en_GB |
dc.description.sponsorship | Leading Technology of Jiangsu Basic Research Plan | en_GB |
dc.description.sponsorship | European Union Horizon 2020 | en_GB |
dc.identifier.citation | CSE 2021: 24th IEEE International Conference on Computational Science and Engineering, Shenyang, China, 20 A- 22 October 2021 | en_GB |
dc.identifier.doi | 10.1109/CSE53436.2021.00034 | |
dc.identifier.grantnumber | 2018YFB2100804 | en_GB |
dc.identifier.grantnumber | 92067206 | en_GB |
dc.identifier.grantnumber | 61972222 | en_GB |
dc.identifier.grantnumber | 61902178 | en_GB |
dc.identifier.grantnumber | 2019WK2071 | en_GB |
dc.identifier.grantnumber | BK20190295 | en_GB |
dc.identifier.grantnumber | BK20192003 | en_GB |
dc.identifier.grantnumber | 898588 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/126528 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2021 IEEE | en_GB |
dc.subject | federated learning | en_GB |
dc.subject | investment | en_GB |
dc.subject | computing | en_GB |
dc.subject | network | en_GB |
dc.title | Exploring investment strategies for federated learning infrastructure in medical care | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2021-07-26T06:53:46Z | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2021-06-07 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2021-06-07 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2021-07-22T12:35:57Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2022-03-25T14:07:45Z | |
refterms.panel | B | en_GB |