2 citations · 3 across the 3 of their papers we have counts for
8 papers
Decentralized Multi-AGV Task Allocation based on Multi-Agent Reinforcement Learning with Information Potential Field Rewards
Mengyuan Li, Bin Guo, Jiangshan Zhang +5
Automated Guided Vehicles (AGVs) have been widely used for material handling in flexible shop floors. Each product requires various raw materials to complete the assembly in produc…
Privacy Threats Analysis to Secure Federated Learning
Yuchen Li, Yifan Bao, Liyao Xiang +4
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties. It is renowned for preserving privacy as the data never le…
Improved Matrix Gaussian Mechanism for Differential Privacy
Jungang Yang, Liyao Xiang, Weiting Li +2
The wide deployment of machine learning in recent years gives rise to a great demand for large-scale and high-dimensional data, for which the privacy raises serious concern. Differ…
Privacy-Preserving Federated Learning on Partitioned Attributes
Shuang Zhang, Liyao Xiang, Xi Yu +4
Real-world data is usually segmented by attributes and distributed across different parties. Federated learning empowers collaborative training without exposing local data or model…
High-Order Relation Construction and Mining for Graph Matching
Hui Xu, Liyao Xiang, Youmin Le +4
Graph matching pairs corresponding nodes across two or more graphs. The problem is difficult as it is hard to capture the structural similarity across graphs, especially on large g…
Rotation-Equivariant Neural Networks for Privacy Protection
Hao Zhang, Yiting Chen, Haotian Ma +5
In order to prevent leaking input information from intermediate-layer features, this paper proposes a method to revise the traditional neural network into the rotation-equivariant…