INTELLECT

Integrative Network for Technology-Enabled Learning, Early-warning, and Continuous Training.

This project unifies the following pillars:

• C - Continual learning
• K - Knowledge distillation
• A - Automated feedback for incremental learning
• E - Early-warning system
• D - Anomaly detection

The project's focus is on leveraging technology and integrated networks to enable ongoing learning, early detection of anomalies, and continuous training.

Cite

Publications:

1. BigData2023: S. Magnani, S. Braghin, A. Rawat, R. Doriguzzi-Corin, M. Purcell and D. Siracusa, "Pruning Federated Learning Models for Anomaly Detection in Resource-Constrained Environments," 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023, pp. 3274-3283, doi: 10.1109/BigData59044.2023.10386446
2. NetSoft2024 (Ongoing)
3. Computer&Security2024 (Ongoing)

Acknowledgements

• Domenico Siracusa, my PhD supervisor and co-advisor and head of the RISING research unit at Fondazione Bruno Kessler.
• Roberto Doriguzzi-Corin, my PhD co-advisor from the RISING research unit at Fondazione Bruno Kessler.
• Stefano Braghin, my mentor from the Security and Privacy unit at IBM Research Europe.
• Ambrish Rawat, advisor and collaborator from the Security and Privacy unit at IBM Research Europe.
• Liubov Nedoshivina, collaborator from the Security and Privacy unit at IBM Research Europe.
• Mark Purcell, head of the Security and Privacy unit at IBM Research Europe.
• Seshu Tirupathi, collaborator from the Interactive AI unit at IBM Research Europe.