3 citations · 4 across the 4 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2024
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
Hongrui Shi, Valentin Radu, Po Yang
With the rapid expansion of edge devices, such as IoT devices, where crucial data needed for machine learning applications is generated, it becomes essential to promote their parti…
cs.LG2022
Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning
Hongrui Shi, Valentin Radu, Po Yang
The heterogeneity of hardware and data is a well-known and studied problem in the community of Federated Learning (FL) as running under heterogeneous settings. Recently, custom-siz…
cs.LG2021★ 3 cited
Data Selection for Efficient Model Update in Federated Learning
Hongrui Shi, Valentin Radu
The Federated Learning (FL) workflow of training a centralized model with distributed data is growing in popularity. However, until recently, this was the realm of contributing cli…