42 citations · 91 across the 11 of their papers we have counts for
6 papers · 1 filter
Modeling Global Distribution for Federated Learning with Label Distribution Skew
Tao Sheng, Chengchao Shen, Yuan Liu +3
Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more…
Generalized Federated Learning via Sharpness Aware Minimization
Zhe Qu, Xingyu Li, Rui Duan +3
Federated Learning (FL) is a promising framework for performing privacy-preserving, distributed learning with a set of clients. However, the data distribution among clients often e…
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…
Context-Aware Online Client Selection for Hierarchical Federated Learning
Zhe Qu, Rui Duan, Lixing Chen +3
Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). Using Edge Ser…
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation
Xingyu Li, Zhe Qu, Bo Tang +1
Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data. Ho…
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…