14 citations · 24 across the 10 of their papers we have counts for
4 papers · 1 filter
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
Ji Liu, Juncheng Jia, Hong Zhang +5
Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this pape…
Distributed and Deep Vertical Federated Learning with Big Data
Ji Liu, Xuehai Zhou, Lei Mo +5
In recent years, data are typically distributed in multiple organizations while the data security is becoming increasingly important. Federated Learning (FL), which enables multipl…
Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources
Ji Liu, Daxiang Dong, Xi Wang +5
Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which ex…
Data Placement for Multi-Tenant Data Federation on the Cloud
Ji Liu, Lei Mo, Sijia Yang +4
Due to privacy concerns of users and law enforcement in data security and privacy, it becomes more and more difficult to share data among organizations. Data federation brings new…