An Approach to Federated Learning of Explainable Fuzzy Regression Models

Jose Luis Corcuera Barcena, Pietro Ducange, Alessio Ercolani, Francesco Marcelloni, Alessandro Renda

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

16 Scopus citations

Abstract

Federated Learning (FL) has been proposed as a privacy preserving paradigm for collaboratively training AI models: in an FL scenario data owners learn a shared model by aggregating locally-computed partial models, with no need to share their raw data with other parties. Although FL is today extensively studied, a few works have discussed federated approaches to generate explainable AI (XAI) models. In this context, we propose an FL approach to learn Takagi-Sugeno-Kang Fuzzy Rule-based Systems (TSK-FRBSs), which can be considered as XAI models in regression problems. In particular, a number of independent data owner nodes participate in the learning process, where each of them generates its own local TSK-FRBS by exploiting an ad-hoc defined procedure. Then, these models are forwarded to a server that is responsible for aggregating them and generating a global TSK-FRBS, which is sent back to the nodes. An appropriate aggregation strategy is proposed to preserve the explainability of the global TSK-FRBS. A thorough experimental analysis highlights that the proposed approach brings benefits, in terms of accuracy, to data owners participating in the federation preserving the privacy of the data. Indeed, the accuracy achieved by the global TSK-FRBS is higher than the ones of the TSK-FRBSs learned by exploiting only local training data.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665467100
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2022-July
ISSN (Print)1098-7584

Conference

Conference2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22

Keywords

  • explainability
  • federated learning
  • regression
  • TSK fuzzy system

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