Alessandro Renda

selected publications

whole list here

1. COMCOM

   Enabling federated learning of explainable AI models within beyond-5G/6G networks

   Bárcena, J. L. Corcuera, Ducange, P., Marcelloni, F., Nardini, G., Noferi, A., Renda, A., Ruffini, F., Schiavo, A., Stea, G., and Virdis, A.

   Computer Communications 2023

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   The quest for trustworthiness in Artificial Intelligence (AI) is increasingly urgent, especially in the field of next-generation wireless networks. Future Beyond 5G (B5G)/6G networks will connect a huge amount of devices and will offer innovative services empowered with AI and Machine Learning tools. Nevertheless, private user data, which are essential for training such services, are not an asset that can be unrestrictedly shared over the network, mainly because of privacy concerns. To overcome this issue, Federated Learning (FL) has recently been proposed as a paradigm to enable collaborative model training among multiple parties, without any disclosure of private raw data. However, the initiative to natively integrate FL services into mobile networks is still far from being accomplished. In this paper we propose a novel FL-as-a-Service framework that provides the B5G/6G network with flexible mechanisms to allow end users to exploit FL services, and we describe its applicability to a Quality of Experience (QoE) forecasting service based on a vehicular networking use case. Specifically, we show how FL of eXplainable AI (XAI) models can be leveraged for the QoE forecasting task, and induces a benefit in terms of both accuracy, compared to local learning, and trustworthiness, thanks to the adoption of inherently interpretable models. Such considerations are supported by an extensive experimental analysis on a publicly available simulated dataset. Finally, we assessed how the learning process is affected by the system deployment and the performance of the underlying communication and computation infrastructure, through system-level simulations, which show the benefits of deploying the proposed framework in edge-based environments.

   @article{comcom2023,
     bibtex_show = {true},
     selected = {true},
     abbr = {COMCOM},
     order = {2023-02},
     author = {Bárcena, J. L. Corcuera and Ducange, P. and Marcelloni, F. and Nardini, G. and Noferi, A. and Renda, A. and Ruffini, F. and Schiavo, A. and Stea, G. and Virdis, A.},
     journal = {Computer Communications},
     title = {Enabling federated learning of explainable AI models within beyond-5G/6G networks},
     volume = {210},
     pages = {356-375},
     year = {2023},
     issn = {0140-3664},
     doi = {10.1016/j.comcom.2023.07.039},
     html = {https://doi.org/10.1016/j.comcom.2023.07.039},
     pdf = {comcom2023.pdf}
   }

2. IEEE TFS

   TSF-DBSCAN: a Novel Fuzzy Density-based Approach for Clustering Unbounded Data Streams

   Bechini, A., Marcelloni, F., and Renda, A.

   IEEE Transactions on Fuzzy Systems 2020

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   In recent years, several clustering algorithms have been proposed with the aim of mining knowledge from streams of data generated at a high speed by a variety of hardware platforms and software applications. Among these algorithms, density-based approaches have proved to be particularly attractive, thanks to their capability of handling outliers and capturing clusters with arbitrary shapes. The streaming setting poses additional challenges that need to be addressed as well: data streams are potentially unbounded and affected by concept drift, i.e. a modification over time in the underlying data generation process. In this paper, we propose Temporal Streaming Fuzzy DBSCAN (TSF-DBSCAN), a novel fuzzy clustering algorithm for streaming data. TSF-DBSCAN is an extension of the well-known DBSCAN algorithm, one of the most popular density-based clustering approaches. Fuzziness is introduced in TSF-DBSCAN to model the uncertainty about the distance threshold that defines the neighborhood of an object. As a consequence, TSF-DBSCAN identifies clusters with fuzzy overlapping borders. A fading model, which makes objects less relevant as they become more remote in time, endows TSF-DBSCAN with the capability of adapting to evolving data streams. The integration of the model in a two-stage approach ensures computational and memory efficiency: during the online stage continuously arriving objects are organized in proper data structures that are later exploited in the offline stage to determine a fine-grained partition. An extensive experimental analysis on synthetic and real world datasets shows that TSF-DBSCAN yields competitive performance when compared to other clustering algorithms recently proposed for streaming data.

   @article{tsf2020,
     bibtex_show = {true},
     author = {Bechini, A. and Marcelloni, F. and Renda, A.},
     abbr = {IEEE TFS},
     journal = {IEEE Transactions on Fuzzy Systems},
     selected = {true},
     order = {2020-12},
     title = {TSF-DBSCAN: a Novel Fuzzy Density-based Approach for Clustering Unbounded Data Streams},
     year = {2020},
     pages = {1-1},
     html = {https://doi.org/10.1109/TFUZZ.2020.3042645},
     doi = {10.1109/TFUZZ.2020.3042645}
   }

3. TAI

   Federated c-means and Fuzzy c-means Clustering Algorithms for Horizontally and Vertically Partitioned Data

   Bárcena, J. L. Corcuera, Marcelloni, F., Renda, A., Bechini, A., and Ducange, P.

   IEEE Transactions on Artificial Intelligence 2024

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   Federated clustering lets multiple data owners collaborate in discovering patterns from distributed data without violating privacy requirements. The federated versions of traditional clustering algorithms proposed so far are, however, “lossy” since they fail to identify exactly the same clusters as the original versions executed on the merged data stored in a centralized server, as would happen if no privacy constraint occurred. In this paper, we propose federated procedures for losslessly executing the C-Means (CM) and the Fuzzy C-Means (FCM) algorithms in both horizontally and vertically partitioned data scenarios, while preserving data privacy. We formally prove that the proposed federated procedures identify the same clusters determined by applying the algorithms to the union of all local data. Further, we present an extensive experimental analysis for characterizing the behavior of the proposed approach in a typical federated learning scenario, that is, as the fraction of participants in the federation changes. We focus on the federated FCM and the horizontally partitioned data, which is the most interesting scenario. We show that the proposed procedure is effective and is able to achieve competitive performance with respect to two recently proposed versions of federated FCM for horizontally partitioned data.

   @article{tai2024,
     author = {Bárcena, J. L. Corcuera and Marcelloni, F. and Renda, A. and Bechini, A. and Ducange, P.},
     title = {Federated c-means and Fuzzy c-means Clustering Algorithms for Horizontally and Vertically Partitioned Data},
     year = {2024},
     journal = {IEEE Transactions on Artificial Intelligence},
     pages = {1–15},
     abbr = {TAI},
     doi = {10.1109/TAI.2024.3426408},
     url = {https://ieeexplore.ieee.org/document/10595840},
     html = {https://ieeexplore.ieee.org/document/10595840},
     pdf = {tai2024},
     code = {https://github.com/Unipisa/FederatedClustering},
     bibtex_show = {true},
     selected = {true},
     order = {2024-07}
   }

4. INFFUS

   Increasing trust in AI through privacy preservation and model explainability: Federated Learning of Fuzzy Regression Trees

   Bárcena, J. L. Corcuera, Ducange, P., Marcelloni, F., and Renda, A.

   Information Fusion 2025

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   Federated Learning (FL) lets multiple data owners collaborate in training a global model without any violation of data privacy, which is a crucial requirement for enhancing users’ trust in Artificial Intelligence (AI) systems. Despite the significant momentum recently gained by the FL paradigm, most of the existing approaches in the field neglect another key pillar for the trustworthiness of AI systems, namely explainability. In this paper, we propose a novel approach for FL of fuzzy regression trees (FRTs), which are generally acknowledged as highly interpretable by-design models. The proposed FL procedure is designed for the scenario of horizontally partitioned data and is based on the transmission of aggregated statistics from the clients to a central server for the tree induction procedure. It is shown that the proposed approach faithfully approximates the ideal case in which the tree induction algorithm is applied on the union of all local datasets, while still ensuring privacy preservation. Furthermore, the FL approach brings benefits, in terms of generalization capability, compared to the local learning setting in which each participant learns its own FRT based only on the private, local, dataset. The adoption of linear models in the leaf nodes ensures a competitive level of performance, as assessed by an extensive experimental campaign on benchmark datasets. The analysis of the results covers both the aspects of accuracy and interpretability of FRT. Finally, we discuss the application of the proposed federated FRT to the task of Quality of Experience forecasting in an automotive case-study.

   @article{inffus2025,
     bibtex_show = {true},
     author = {Bárcena, J. L. Corcuera and Ducange, P. and Marcelloni, F. and Renda, A.},
     title = {Increasing trust in AI through privacy preservation and model explainability: Federated Learning of Fuzzy Regression Trees},
     year = {2025},
     journal = {Information Fusion},
     volume = {113},
     abbr = {INFFUS},
     doi = {10.1016/j.inffus.2024.102598},
     url = {https://www.sciencedirect.com/science/article/pii/S1566253524003762},
     html = {https://www.sciencedirect.com/science/article/pii/S1566253524003762},
     code = {https://github.com/Unipisa/FederatedFuzzyRegressionTree},
     pdf = {inffus2025.pdf},
     selected = {true},
     order = {2025-01}
   }