1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning
Wujun Zhou, Shu Ding, ZeLin Li +1
Federated learning enables the clients to collaboratively train a global model, which is aggregated from local models. Due to the heterogeneous data distributions over clients and…
cs.LG2022★ 1 cited
Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
Lefan Zhang, Zhang-Hao Tian, Wujun Zhou +1
The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Many methods have been…