1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Aggregation in the Mirror Space (AIMS): Fast, Accurate Distributed Machine Learning in Military Settings
Ryan Yang, Haizhou Du, Andre Wibisono +1
Distributed machine learning (DML) can be an important capability for modern military to take advantage of data and devices distributed at multiple vantage points to adapt and lear…
cs.LG2022★ 1 cited
Achieving Efficient Distributed Machine Learning Using a Novel Non-Linear Class of Aggregation Functions
Haizhou Du, Ryan Yang, Yijian Chen +3
Distributed machine learning (DML) over time-varying networks can be an enabler for emerging decentralized ML applications such as autonomous driving and drone fleeting. However, t…
cs.LG2021
Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression
Haizhou Du, Xiaojie Feng, Qiao Xiang +1
A fundamental issue for federated learning (FL) is how to achieve optimal model performance under highly dynamic communication environments. This issue can be alleviated by the fac…