1 citations · 1 across the 4 of their papers we have counts for
4 papers
From Transfer to Collaboration: A Federated Framework for Cross-Market Sequential Recommendation
Jundong Chen, Honglei Zhang, Xiangmou Qu +3
Cross-market recommendation (CMR) aims to enhance recommendation performance across multiple markets. Due to its inherent characteristics, i.e., data isolation, non-overlapping use…
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
Zhilong Li, Xiaohu Wu, Xiaoli Tang +6
There is growing research interest in measuring the statistical heterogeneity of clients' local datasets. Such measurements are used to estimate the suitability for collaborative t…
Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning
Mengmeng Chen, Xiaohu Wu, Xiaoli Tang +5
Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data het…
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Shanli Tan, Hao Cheng, Xiaohu Wu +5
Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to selec…