623 citations · 1.1k across the 16 of their papers we have counts for
16 papers
EdgeDis: Enabling Fast, Economical, and Reliable Data Dissemination for Mobile Edge Computing
Bo Li, Qiang He, Feifei Chen +3
Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters awa…
Privacy-preserving design of graph neural networks with applications to vertical federated learning
Ruofan Wu, Mingyang Zhang, Lingjuan Lyu +6
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, h…
FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models
Yingqian Cui, Jie Ren, Yuping Lin +7
Text-to-image generative models, especially those based on latent diffusion models (LDMs), have demonstrated outstanding ability in generating high-quality and high-resolution imag…
Federated Learning over a Wireless Network: Distributed User Selection through Random Access
Chen Sun, Shiyao Ma, Ce Zheng +3
User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional sy…
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
Xiaolong Xu, Lingjuan Lyu, Yihong Dong +3
With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels w…
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
Shuhe Wang, Yuxian Meng, Rongbin Ouyang +4
To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL mode…
Protecting Split Learning by Potential Energy Loss
Fei Zheng, Chaochao Chen, Lingjuan Lyu +5
As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry. However, its security is constantly being questioned since the…
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks
Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi +2
Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowl…
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Ruixuan Liu, Yanlin Wang, Yang Cao +4
Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the prob…
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
Jamie Cui, Chaochao Chen, Lingjuan Lyu +2
Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that a…
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang +2
Since training a large-scale backdoored model from scratch requires a large training dataset, several recent attacks have considered to inject backdoors into a trained clean model…
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu +2
Federated learning is widely used to learn intelligent models from decentralized data. In federated learning, clients need to communicate their local model updates in each iteratio…
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Yang Cao +2
Graph neural network (GNN) is widely used for recommendation to model high-order interactions between users and items. Existing GNN-based recommendation methods rely on centralized…
FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT
J. Li, L. Lyu, X. Liu +2
The distributed denial of service (DDoS) attack is detrimental to the industrial Internet of things (IIoT) as it triggers severe resource starvation on networked objects. Recent dy…
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
Xinyi Xu, Lingjuan Lyu
Federated learning (FL) is an emerging practical framework for effective and scalable machine learning among multiple participants, such as end users, organizations and companies.…
FederBoost: Private Federated Learning for GBDT
Zhihua Tian, Rui Zhang, Xiaoyang Hou +4
Federated Learning (FL) has been an emerging trend in machine learning and artificial intelligence. It allows multiple participants to collaboratively train a better global model a…