3 papers
cs.DC2025
An Online Fragmentation-Aware GPU Scheduler for Multi-Tenant MIG-based Clouds
Marco Zambianco, Lorenzo Fasol, Roberto Doriguzzi-Corin
The explosive growth of AI applications has created unprecedented demand for GPU resources. Cloud providers meet this demand through GPU-as-a-Service platforms that offer rentable…
cs.CR2025
Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Roberto Doriguzzi-Corin, Petr Sabel, Silvio Cretti +1
Machine Learning (ML) techniques have shown strong potential for network traffic analysis; however, their effectiveness depends on access to representative, up-to-date datasets, wh…
cs.CR2025
Adaptive Federated Learning with Functional Encryption: A Comparison of Classical and Quantum-safe Options
Enrico Sorbera, Federica Zanetti, Giacomo Brandi +3
Federated Learning (FL) is a collaborative method for training machine learning models while preserving the confidentiality of the participants' training data. Nevertheless, FL is…