64 citations · 72 across the 4 of their papers we have counts for
5 papers
SparsyFed: Sparse Adaptive Federated Training
Adriano Guastella, Lorenzo Sani, Alex Iacob +3
Sparse training is often adopted in cross-device federated learning (FL) environments where constrained devices collaboratively train a machine learning model on private data by ex…
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-Gradients
Alessio Mora, Carlo Mazzocca, Rebecca Montanari +1
The right to be forgotten is a fundamental principle of privacy-preserving regulations and extends to Machine Learning (ML) paradigms such as Federated Learning (FL). While FL enha…
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher
Alessio Mora, Lorenzo Valerio, Paolo Bellavista +1
Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data. To comply with the right to be forgotte…
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics
Nicolò Romandini, Alessio Mora, Carlo Mazzocca +2
Federated learning (FL) enables collaborative training of a machine learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy b…
Knowledge Distillation for Federated Learning: a Practical Guide
Alessio Mora, Irene Tenison, Paolo Bellavista +1
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. The most used algorithms for FL are parameter-averagi…