@inproceedings{a1c6805d6ac74c60a365e050072b04b7,
title = "Federated learning with uncertainty on the example of a medical data",
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.",
author = "K. Dyczkowski and B. Pckala and J. Szkola and A. Wilbik",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2022 ; Conference date: 18-07-2022 Through 23-07-2022",
year = "2022",
doi = "10.1109/FUZZ-IEEE55066.2022.9882862",
language = "English",
isbn = "9781665467100",
series = "IEEE International Fuzzy Systems Conference Proceedings",
publisher = "IEEE",
booktitle = "2022 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) - Proceedings",
address = "United States",
}