An Approach to Federated Learning of Explainable Fuzzy Regression Models

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

15 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665467100
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Padua, Italia
Duración: 18 jul. 202223 jul. 2022

Serie de la publicación

NombreIEEE International Conference on Fuzzy Systems
Volumen2022-July
ISSN (versión impresa)1098-7584

Conferencia

Conferencia2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022
País/TerritorioItalia
CiudadPadua
Período18/07/2223/07/22

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