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cs.LG2025
Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks
Giulia Fracastoro, Sophie M. Fosson, Andrea Migliorati +1
The design of sparse neural networks, i.e., of networks with a reduced number of parameters, has been attracting increasing research attention in the last few years. The use of spa…
cs.LG2024
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information
Emanuel Buttaci, Giuseppe Carlo Calafiore
Federated learning has emerged in the last decade as a distributed optimization paradigm due to the rapidly increasing number of portable devices able to support the heavy computat…