Search Results for author: Yilun Jin

Found 14 papers, 4 papers with code

A Survey on Vertical Federated Learning: From a Layered Perspective

no code implementations4 Apr 2023 Liu Yang, Di Chai, Junxue Zhang, Yilun Jin, Leye Wang, Hao liu, Han Tian, Qian Xu, Kai Chen

From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL.

Federated Learning Privacy Preserving

Federated Learning without Full Labels: A Survey

no code implementations25 Mar 2023 Yilun Jin, Yang Liu, Kai Chen, Qiang Yang

Therefore, the problem of federated learning without full labels is important in real-world FL applications.

Federated Learning Self-Supervised Learning +1

Secure Forward Aggregation for Vertical Federated Neural Networks

no code implementations28 Jun 2022 Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin, Leye Wang, Kun Guo, Kai Chen

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.

Federated Learning Privacy Preserving

Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

1 code implementation7 Apr 2021 Qingqing Long, Yilun Jin, Yi Wu, Guojie Song

However, the inability of GNNs to model substructures in graphs remains a significant drawback.

Graph Mining

Ternary Hashing

no code implementations16 Mar 2021 Chang Liu, Lixin Fan, Kam Woh Ng, Yilun Jin, Ce Ju, Tianyu Zhang, Chee Seng Chan, Qiang Yang

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.


Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness

no code implementations27 Nov 2020 Yilun Jin, Lixin Fan, Kam Woh Ng, Ce Ju, Qiang Yang

Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed.

EPNE: Evolutionary Pattern Preserving Network Embedding

no code implementations24 Sep 2020 Junshan Wang, Yilun Jin, Guojie Song, Xiaojun Ma

In this paper, we propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes.

Network Embedding

Graph Structural-topic Neural Network

1 code implementation25 Jun 2020 Qingqing Long, Yilun Jin, Guojie Song, Yi Li, Wei. Lin

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.

Topic Models

Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective

no code implementations26 Feb 2020 Yilun Jin, Xiguang Wei, Yang Liu, Qiang Yang

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.

BIG-bench Machine Learning Federated Learning

GraLSP: Graph Neural Networks with Local Structural Patterns

no code implementations18 Nov 2019 Yilun Jin, Guojie Song, Chuan Shi

Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns.

Graph Representation Learning

DANE: Domain Adaptive Network Embedding

2 code implementations3 Jun 2019 Yizhou Zhang, Guojie Song, Lun Du, Shu-wen Yang, Yilun Jin

Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.

Domain Adaptation Network Embedding

SecureBoost: A Lossless Federated Learning Framework

1 code implementation25 Jan 2019 Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Dimitrios Papadopoulos, Qiang Yang

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.

BIG-bench Machine Learning Entity Alignment +2

Cannot find the paper you are looking for? You can Submit a new open access paper.