most citedSubmodular Maximization Approaches for Equitable Client Selection in Federated Learning

2 citations · 2 across the 2 of their papers we have counts for

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2 papers

cs.LG2024★ 2 cited

Submodular Maximization Approaches for Equitable Client Selection in Federated Learning

Andrés Catalino Castillo Jiménez, Ege C. Kaya, Lintao Ye +1

In a conventional Federated Learning framework, client selection for training typically involves the random sampling of a subset of clients in each iteration. However, this random…

math.OC2023

Maximization of Nonsubmodular Functions under Multiple Constraints with Applications

Lintao Ye, Zhi-Wei Liu, Ming Chi +1

We consider the problem of maximizing a monotone nondecreasing set function under multiple constraints, where the constraints are also characterized by monotone nondecreasing set f…