From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL.
Therefore, the problem of federated learning without full labels is important in real-world FL applications.
In this paper, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN.
Data-driven approaches have been applied to many problems in urban computing.
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.
In this paper, we propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes.
Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently.
Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner.
Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns.
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.
This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.
The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets.