Autor:

Szkoła, Jarosław ; Pękala, Barbara ; Dyczkowski, Krzysztof

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

Managing uncertainty in federated learning via interval fuzzy sets and entropy-based fusion

Tytuł publikacji grupowej:

AMCS, volume 36 (2026)

Temat i słowa kluczowe:

federated learning ; interval-valued fuzzy sets ; uncertainty modeling ; Choquet integral ; entropy-based aggregation ; interval logistic regression ; medical diagnostics ; non-IID data

Abstract:

This paper introduces a federated learning framework designed to improve the reliability of diagnostic models under conditions of uncertainty, with a particular focus on medical applications such as breast cancer diagnosis. The proposed method integrates interval-valued fuzzy sets to capture data imprecision and employs logistic regression enhanced with interval-based parameter estimation. ; Model parameters are aggregated across clients using the Choquet integral, extended with an entropy-based weighting scheme that accounts for both model performance and uncertainty. Experimental results on the Wisconsin breast cancer dataset demonstrate that the proposed federated architecture achieves superior performance compared to traditional methods, particularly in non-IID and unbalanced data scenarios. The framework offers robust privacy preservation, effective uncertainty modeling, and improved classification accuracy, making it suitable for high-stakes, privacy-sensitive domains.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2026

Typ zasobu:

artykuł

DOI:

10.61822/amcs-2026-0015

Strony:

211-221

Źródło:

AMCS, volume 36, number 2 (2026) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego