3 papers
cs.LG2026
Neural Network Compression by Approximate Differential Equivalence
Ravi Dhiman, Andrea Passarella, Mirco Tribastone +1
Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a complementary approach that comp…
cs.LG2026
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…
cs.LG2026
Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions
Mirko Nardi, Lorenzo Valerio, Andrea Passarella
Federated Learning (FL) enables decentralized machine learning while preserving data privacy, making it ideal for sensitive applications where data cannot be shared. While FL has b…