@inproceedings{chen2020,
  title={Asynchronous online federated learning for edge devices with non-iid data},
  author={Chen, Yujing and Ning, Yue and Slawski, Martin and Rangwala, Huzefa},
  booktitle={2020 IEEE International Conference on Big Data (Big Data)},
  pages={15--24},
  year={2020}
}

@inproceedings{yoon2021,
  title={Federated continual learning with weighted inter-client transfer},
  author={Yoon, Jaehong and Jeong, Wonyong and Lee, Giwoong and Yang, Eunho and Hwang, Sung Ju},
  booktitle={International Conference on Machine Learning},
  pages={12073--12086},
  year={2021}
}

@inproceedings{odeyomi2021,
  title={Differentially-private federated learning with long-term constraints using online mirror descent},
  author={Odeyomi, Olusola and Zaruba, Gergely},
  booktitle={2021 IEEE International Symposium on Information Theory (ISIT)},
  pages={1308--1313},
  year={2021},
  organization={IEEE}
}

@InProceedings{marfoq23a,
  title = 	 {Federated Learning for Data Streams},
  author =       {Marfoq, Othmane and Neglia, Giovanni and Kameni, Laetitia and Vidal, Richard},
  booktitle = 	 {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {8889--8924},
  year = 	 {2023},
  volume = 	 {206},
}

@article{sanchez2024federatedtrust,
  title={Federatedtrust: A solution for trustworthy federated learning},
  author={S{\'a}nchez, Pedro Miguel S{\'a}nchez and Celdr{\'a}n, Alberto Huertas and Xie, Ning and Bovet, G{\'e}r{\^o}me and P{\'e}rez, Gregorio Mart{\'\i}nez and Stiller, Burkhard},
  journal={Future Generation Computer Systems},
  volume={152},
  pages={83--98},
  year={2024},
  publisher={Elsevier}
}

@article{ma2022privacy,
  title={Privacy-preserving federated learning based on multi-key homomorphic encryption},
  author={Ma, Jing and Naas, Si-Ahmed and Sigg, Stephan and Lyu, Xixiang},
  journal={International Journal of Intelligent Systems},
  volume={37},
  number={9},
  pages={5880--5901},
  year={2022},
  publisher={Wiley Online Library}
}

@article{cordon2001evolutionary,
  title={Evolutionary tuning and learning of fuzzy knowledge bases},
  author={Cord{\'o}n, Oscar and Herrera, F and Hoffmann, F and Magdalena, L},
  journal={Genetic fuzzy systems},
  volume={19},
  year={2001},
  publisher={World Scientific Singapore}
}

@article{zadeh1975concept,
  title={The concept of a linguistic variable and its application to approximate reasoning—I},
  author={Zadeh, Lotfi Asker},
  journal={Information sciences},
  volume={8},
  number={3},
  pages={199--249},
  year={1975},
  publisher={Elsevier}
}

@article{miller1995genetic,
  title={Genetic algorithms, tournament selection, and the effects of noise},
  author={Miller, Brad L and Goldberg, David E and others},
  journal={Complex systems},
  volume={9},
  number={3},
  pages={193--212},
  year={1995},
  publisher={[Champaign, IL, USA: Complex Systems Publications, Inc., c1987-}
}

@book{holland1992adaptation,
  title={Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence},
  author={Holland, John H},
  year={1992},
  publisher={MIT press}
}

@article{garcia2018moea,
  title={MOEA-EFEP: Multi-objective evolutionary algorithm for extracting fuzzy emerging patterns},
  author={Garc{\'\i}a-Vico, {\'A}ngel Miguel and Carmona, Crist{\'o}bal Jos{\'e} and Gonz{\'a}lez, Pedro and del Jesus, Mar{\'\i}a Jos{\'e}},
  journal={IEEE Transactions on Fuzzy Systems},
  volume={26},
  number={5},
  pages={2861--2872},
  year={2018},
  publisher={IEEE}
}

@InProceedings{pmlr-v11-bifet10a,
  title = 	 {MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering},
  author = 	 {Bifet, Albert and Holmes, Geoff and Pfahringer, Bernhard and Kranen, Philipp and Kremer, Hardy and Jansen, Timm and Seidl, Thomas},
  booktitle = 	 {Proceedings of the First Workshop on Applications of Pattern Analysis},
  pages = 	 {44--50},
  year = 	 {2010},
  volume = 	 {11},
  series = 	 {Proceedings of Machine Learning Research},
}

@article{FormaTriangular,
title = {The concept of a linguistic variable and its application to approximate reasoning—I},
journal = {Information Sciences},
volume = {8},
number = {3},
pages = {199-249},
year = {1975},
issn = {0020-0255},
doi = {10.1016/0020-0255(75)90036-5},
author = {L.A. Zadeh},
}

@article{599,
  keywords = {Federated rule learning, Trustworthy artificial intelligence, Data streaming, Supervised descriptive rules},
  author = {María Asunción Padilla Rascón and Ángel Miguel García-Vico and Cristóbal J. Carmona},
  title = {Trustworthy and explainable federated system for extracting descriptive rules in a data streaming environment},
  abstract = {A connected world in an information age with dozens of connected devices per person constantly generates continuous data streams. This leads us to the need to generate new intelligent models that discover knowledge in complex paradigms. However, these complex paradigms (capable of generating knowledge in isolated devices and sharing it between them, known as federated learning) must comply with the guidelines of Trustworthy Artificial Intelligence that obtains models with high levels of security, privacy, explainability and traceability. This contribution introduces the Trustworthy and Explainable Federated System based on Supervised Descriptive Rules for Data Streaming (TEFeS-SDR) algorithm, a trustworthy and explainable federated system for extracting descriptive rules in streaming data environments. This model, based on federated learning, emphasizes privacy and security through binary encoding and asymmetric encryption, avoiding the transfer of raw data between devices. Additionally, the system ensures traceability and auditability of the generated rules, providing transparency and trust. Experimental results demonstrate its capability to handle abrupt changes in data streams (concept drift) while maintaining high-quality and homogeneous global models. This work advances the path towards responsible artificial intelligence by combining explainability, security, and efficiency in dynamic environments.},
  year = {2025},
  journal = {Results in Engineering},
  volume = {25},
  pages = {104137},
  issn = {2590-1230},
  url = {https://www.sciencedirect.com/science/article/pii/S2590123025002257},
  doi = {https://doi.org/10.1016/j.rineng.2025.104137},
}