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cs.LG2026
On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
Usevalad Milasheuski, Piero Baraldi, Enrico Zio +1
Federated Learning (FL) has emerged as a promising paradigm for preserving client data ownership and control over distributed Internet of Things (IoT) environments. While discrimin…
cs.LG2025
Bayesian Federated Learning for Continual Training
Usevalad Milasheuski, Luca Barbieri, Sanaz Kianoush +2
Bayesian Federated Learning (BFL) enables uncertainty quantification and robust adaptation in distributed learning. In contrast to the frequentist approach, it estimates the poster…
cs.LG2024
On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks
Usevalad Milasheuski, Luca Barbieri, Bernardo Camajori Tedeschini +2
Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of t…