1 citations · 1 across the 8 of their papers we have counts for
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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…
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…
DecHW: Heterogeneous Decentralized Federated Learning Exploiting Second-Order Information
Adnan Ahmad, Chiara Boldrini, Lorenzo Valerio +2
Decentralized Federated Learning (DFL) is a serverless collaborative machine learning paradigm where devices collaborate directly with neighbouring devices to exchange model inform…
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…
Robustness of Decentralised Learning to Nodes and Data Disruption
Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio +3
In the vibrant landscape of AI research, decentralised learning is gaining momentum. Decentralised learning allows individual nodes to keep data locally where they are generated an…
The Built-In Robustness of Decentralized Federated Averaging to Bad Data
Samuele Sabella, Chiara Boldrini, Lorenzo Valerio +2
Decentralized federated learning (DFL) enables devices to collaboratively train models over complex network topologies without relying on a central controller. In this setting, loc…