575 citations · 1k across the 18 of their papers we have counts for
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cs.LG2020★ 75 cited
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
Yae Jee Cho, Jianyu Wang, Gauri Joshi
Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several…
cs.LG2020★ 575 cited
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Jianyu Wang, Qinghua Liu, Hao Liang +2
In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client i…
cs.LG2020★ 3 cited
Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD
Jianyu Wang, Hao Liang, Gauri Joshi
Distributed stochastic gradient descent (SGD) is essential for scaling the machine learning algorithms to a large number of computing nodes. However, the infrastructures variabilit…