1 citations · 1 across the 6 of their papers we have counts for
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A Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex Models and Heterogeneous Data
Haizhou Du, Chengdong Ni
Emerging distributed applications recently boosted the development of decentralized machine learning, especially in IoT and edge computing fields. In real-world scenarios, the comm…
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…
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…
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning
Haizhou Du, Zong Yan, Qiao Xiang +1
Steiner Tree Problem (STP) in graphs aims to find a tree of minimum weight in the graph that connects a given set of vertices. It is a classic NP-hard combinatorial optimization pr…
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…