1 citations · 1 across the 5 of their papers we have counts for
5 papers
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
Mengmeng Chen, Xiaohu Wu, Qiqi Liu +5
Multi-objective optimization (MOO) exists extensively in machine learning, and aims to find a set of Pareto-optimal solutions, called the Pareto front, e.g., it is fundamental for…
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…
Learning Structurally Stabilized Representations for Multi-modal Lossless DNA Storage
Ben Cao, Tiantian He, Xue Li +4
In this paper, we present Reed-Solomon coded single-stranded representation learning (RSRL), a novel end-to-end model for learning representations for multi-modal lossless DNA stor…
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…