26 citations · 81 across the 29 of their papers we have counts for
13 papers · 1 filter
Federated Learning for Short Text Clustering
Mengling Hu, Chaochao Chen, Weiming Liu +2
Short text clustering has been popularly studied for its significance in mining valuable insights from many short texts. In this paper, we focus on the federated short text cluster…
Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering
Mengling Hu, Chaochao Chen, Weiming Liu +3
Graph clustering has been popularly studied in recent years. However, most existing graph clustering methods focus on node-level clustering, i.e., grouping nodes in a single graph…
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
Yuyuan Li, Chaochao Chen, Xiaolin Zheng +4
With the growing privacy concerns in recommender systems, recommendation unlearning, i.e., forgetting the impact of specific learned targets, is getting increasing attention. Exist…
In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
Zhongxuan Han, Chaochao Chen, Xiaolin Zheng +4
Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The ex…
Defending Label Inference Attacks in Split Learning under Regression Setting
Haoze Qiu, Fei Zheng, Chaochao Chen +1
As a privacy-preserving method for implementing Vertical Federated Learning, Split Learning has been extensively researched. However, numerous studies have indicated that the priva…
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
Xinting Liao, Chaochao Chen, Weiming Liu +7
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective i…
Decentralized Graph Neural Network for Privacy-Preserving Recommendation
Xiaolin Zheng, Zhongyu Wang, Chaochao Chen +2
Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentrali…
Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks
Xiaolin Zheng, Zhongyu Wang, Chaochao Chen +2
The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Alth…
Integration of Large Language Models and Federated Learning
Chaochao Chen, Xiaohua Feng, Yuyuan Li +4
As the parameter size of Large Language Models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has…
Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation
Yuyuan Li, Jiaming Zhang, Yixiu Liu +1
Privacy concerns associated with machine learning models have driven research into machine unlearning, which aims to erase the memory of specific target training data from already…
Federated Learning on Non-iid Data via Local and Global Distillation
Xiaolin Zheng, Senci Ying, Fei Zheng +4
Most existing federated learning algorithms are based on the vanilla FedAvg scheme. However, with the increase of data complexity and the number of model parameters, the amount of…
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering
Xiaolin Zheng, Mengling Hu, Weiming Liu +2
Short text clustering is challenging since it takes imbalanced and noisy data as inputs. Existing approaches cannot solve this problem well, since (1) they are prone to obtain dege…
PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation
Xinting Liao, Weiming Liu, Xiaolin Zheng +2
Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better perf…