623 citations · 1.1k across the 16 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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.…