167 citations · 393 across the 9 of their papers we have counts for
9 papers
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
Syed Irfan Ali Meerza, Luyang Liu, Jiaxin Zhang +1
Federated learning (FL) has emerged as a prospective solution for collaboratively learning a shared model across clients without sacrificing their data privacy. However, the federa…
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li +3
Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies ha…
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction
Samiul Alam, Luyang Liu, Ming Yan +1
Most cross-device federated learning (FL) studies focus on the model-homogeneous setting where the global server model and local client models are identical. However, such constrai…
Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services
Luyang Liu, David Racz, Kara Vaillancourt +9
Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which…
Cross-Lingual Language Model Meta-Pretraining
Zewen Chi, Heyan Huang, Luyang Liu +2
The success of pretrained cross-lingual language models relies on two essential abilities, i.e., generalization ability for learning downstream tasks in a source language, and cros…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…