Federated learning with uncertainty on the example of a medical data

K. Dyczkowski*, B. Pckala, J. Szkola, A. Wilbik

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Abstract

This paper describes a federated learning model capable to process imprecise and missing data. Federation learning is a technique to solve the problem of data governance and privacy by training algorithms without exchanging the data itself. The performance of the proposed method is demonstrated on medical data of breast cancer cases. Results for different data loss scenarios and corresponding measures of classification quality are presented and discussed.
Original languageEnglish
Title of host publication2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) - Proceedings
PublisherIEEE
Number of pages8
ISBN (Print)9781665467100
DOIs
Publication statusPublished - 2022
EventIEEE International Conference on Fuzzy Systems - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

SeriesIEEE International Fuzzy Systems Conference Proceedings
ISSN1544-5615

Conference

ConferenceIEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE 2022
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22

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