38 citations · 72 across the 3 of their papers we have counts for
3 papers
cs.DC2021★ 34 cited
Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
Zhifeng Jiang, Wei Wang, Bo Li +1
The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively trai…
cs.LG2019
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
Ruoxi Jia, Fan Wu, Xuehui Sun +6
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a…
cs.DC2018★ 38 cited
Semi-Dynamic Load Balancing: Efficient Distributed Learning in Non-Dedicated Environments
Chen Chen, Qizhen Weng, Wei Wang +2
Machine learning (ML) models are increasingly trained in clusters with non-dedicated workers possessing heterogeneous resources. In such scenarios, model training efficiency can be…