10 citations · 10 across the 1 of their papers we have counts for
2 papers
cs.LG2026★ 10 cited
Towards Interpretable Federated Learning
Anran Li, Rui Liu, Ming Hu +4
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespre…
cs.LG2025
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
Yuanyuan Chen, Zichen Chen, Pengcheng Wu +1
Large-scale neural networks possess considerable expressive power. They are well-suited for complex learning tasks in industrial applications. However, large-scale models pose sign…