18 citations · 24 across the 4 of their papers we have counts for
4 papers · 1 filter
G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima
Xingyu Li, Bo Tang
Deep neural networks (DNNs) have demonstrated promising results in various complex tasks. However, current DNNs encounter challenges with over-parameterization, especially when the…
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Xingyu Li, Zhe Qu, Shangqing Zhao +3
Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL en…
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective
Chenyang Xi, Bo Tang, Jiajun Shen +3
Offline reinforcement learning (RL) has increasingly become the focus of the artificial intelligent research due to its wide real-world applications where the collection of data ma…
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients
Xingyu Li, Zhe Qu, Bo Tang +1
Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices. Existing methods, e.g., Federated A…