Towards a Federated Fuzzy Learning System

A. Wilbik*, P. Grefen

*Corresponding author for this work

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

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Abstract

The abundant availability of data allows the construction of predictive systems that support decision makers in business and society. A problem arises if an organization does not have a large enough data set by itself to construct a system of adequate quality. In this case, data across organizations has to be used, which introduces risks of data sharing. To overcome these risks, federated learning is getting increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. So far, only crisp systems have been used in this context. The use of a fuzzy inference system can bring advantages to deal with vagueness and uncertainty in predictive systems. Therefore, in this paper we explore the (hopefully) happy marriage of federated learning and fuzzy inference mechanisms. We show that it is indeed possible to build a fuzzy inference model in a federated learning setting, resulting in a Federated Fuzzy Learning System ((FLS)-L-2). We also show that this combination brings advantages to decision making that cannot be achieved with either mechanism in isolation.
Original languageEnglish
Title of host publicationIEEE CIS INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS 2021 (FUZZ-IEEE)
PublisherIEEE
Number of pages6
ISBN (Print)9781665444071
DOIs
Publication statusPublished - 2021
EventIEEE CIS International Conference on Fuzzy Systems (FUZZ-IEEE) - ELECTR NETWORK, Luxembourg, Luxembourg
Duration: 11 Jul 202114 Jul 2021
https://attend.ieee.org/fuzzieee-2021/program-overview/

Publication series

SeriesIEEE International Conference on Fuzzy Systems
ISSN1098-7584

Conference

ConferenceIEEE CIS International Conference on Fuzzy Systems (FUZZ-IEEE)
Country/TerritoryLuxembourg
CityLuxembourg
Period11/07/2114/07/21
Internet address

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