2 citations · 2 across the 1 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024★ 2 cited
Synthetic Data Aided Federated Learning Using Foundation Models
Fatima Abacha, Sin G. Teo, Lucas C. Cordeiro +1
In heterogeneous scenarios where the data distribution amongst the Federated Learning (FL) participants is Non-Independent and Identically distributed (Non-IID), FL suffers from th…
cs.LG2024
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks
Mark Huasong Meng, Hao Guan, Liuhuo Wan +3
We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning. To preserve the model fidelity, PAODING adopts an iterative process…