PUBBLICAZIONI


Internazionali

  • Lorenzo Carnevale, Daniel Balouek, Serena Sebbio, Manish Parashar and Massimo Villari, “Private Distributed Resource Management Data: Predicting CPU Utilization with Bi-LSTM and Federated Learning“. Proceedings of the IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid), Tromsø, Norway, 2025, pp. 266-275 (doi: 10.1109/CCGRID64434.2025.00048).
  • Serena Sebbio, Lorenzo Carnevale, Daniel Balouek, Manish Parashar and Massimo Villari, “Data-Driven Operational Artificial Intelligence for Computing Continuum: A Natural Disaster Management Use Case“. Proceedings of the IEEE 25th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), Tromsø, Norway, 2025, pp. 92-99 (doi: 10.1109/CCGridW65158.2025.00022).
  • Alessio Catalfamo, Lorenzo Carnevale, Marco Garofalo and Massimo Villari. “Flower Full-Compliant Implementation of Federated Learning with Homomorphic Encryption“. Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Paris, France, 2024, pp. 1-5 (doi: 10.1109/ISCC61673.2024.10733641).
  • Pierluigi Dell’Acqua, Maria Fazio, Lorenzo Carnevale and Massimo Villari. “Knowledge Distillation and Federated Learning for Data-Driven Monitoring of Electrical Vehicle Li-Battery“. Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Paris, France, 2024, pp. 1-5. (doi: 10.1109/ISCC61673.2024.10733629).
  • Mark Adrian Gambito, Lorenzo Carnevale, Mohammad Reza Jabbarpour, Bahman Javadi and Massimo Villari. “Hierarchical Federated Learning for Natural Disaster Management“. Proceedings of the IEEE/ACM 17th International Conference on Utility and Cloud Computing (UCC), Sharjah, United Arab Emirates, 2024, pp. 282-289 (doi: 10.1109/UCC63386.2024.00047).
  • Roberto Marino, Antonino Marino, Domenica De Domenico, Lorenzo Carnevale, Mark Adrian Gambito and Massimo Villari. “A Low Cost Platform for Distributed Data-Driven Structural Health Monitoring“. Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Paris, France, 2024, pp. 1-6 (doi: 10.1109/ISCC61673.2024.10733602).
  • Roberto Marino, Lorenzo Carnevale, Maria Fazio, and Massimo Villari. “Make Federated Learning a Standard in Robotics by Using ROS2“. Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies (BDCAT). Association for Computing Machinery, New York, NY, USA, Article 20, 1–6. 2024 (doi: 10.1145/3632366.3632373).
  • Serena Sebbio, Gabriele Morabito, Alessio Catalfamo, Lorenzo Carnevale, and Maria Fazio. “Federated Learning on Raspberry Pi 4: A Comprehensive Power Consumption Analysis“. Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing (UCC). Association for Computing Machinery, New York, NY, USA, Article 44, 1–6. 2024 (doi: 10.1145/3603166.3632545).
  • Lorenzo Carnevale, Antonio Filograna, Francesco Arigliano, Roberto Marino, Armando Ruggeri e Maria Fazio. “Supporting the Natural Disaster Management Distributing Federated Intelligence over the Cloud-Edge Continuum: the TEMA Architecture“. Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies (BDCAT), Taormina, Italy, 2024 (doi: (10.1145/3632366.3632371).
  • Mario Colosi, Marco Garofalo, Lorenzo Carnevale, Roberto Marino, Maria Fazio, and Massimo Villari. “EDGEmergency: A Cloud-Edge Platform to Enable Pervasive Computing for Disaster Management“. Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies (BDCAT). Association for Computing Machinery, New York, NY, USA, Article 19, 1–4, 2024 (doi: 10.1145/3632366.3632372).
  • Roberto Marino, Lorenzo Carnevale e Massimo Villari. “When Robotics Meets Distributed Learning: the Federated Learning Robotic Network Framework“. IEEE Symposium on Computers and Communications (ISCC), Tunis, Tunisia, 2023, pp. 1–6 (doi: 10.1109/ISCC58397.2023)