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20212025
most citedFedHybrid: A Hybrid Primal-Dual Algorithm Framework for Federated Optimization

31 citations · 38 across the 7 of their papers we have counts for

collaborators

7 papers

cs.GT2025

Incentive Analysis for Agent Participation in Federated Learning

Lihui Yi, Xiaochun Niu, Ermin Wei

Federated learning offers a decentralized approach to machine learning, where multiple agents collaboratively train a model while preserving data privacy. In this paper, we investi…

cs.SI2024

Exact Label Recovery in Euclidean Random Graphs

Julia Gaudio, Charlie Guan, Xiaochun Niu +1

In this paper, we propose a family of label recovery problems on weighted Euclidean random graphs. The vertices of a graph are embedded in according to a Poisson poi…

cs.SI2023

Exact Community Recovery in the Geometric SBM

Julia Gaudio, Xiaochun Niu, Ermin Wei

We study the problem of exact community recovery in the Geometric Stochastic Block Model (GSBM), where each vertex has an unknown community label as well as a known position, gener…

cs.LG2023★ 3 cited

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters

Zhenyu Sun, Xiaochun Niu, Ermin Wei

Generalization performance is a key metric in evaluating machine learning models when applied to real-world applications. Good generalization indicates the model can predict unseen…

math.OC2022

DISH: A Distributed Hybrid Optimization Method Leveraging System Heterogeneity

Xiaochun Niu, Ermin Wei

We study distributed optimization problems over multi-agent networks, including consensus and network flow problems. Existing distributed methods neglect the heterogeneity among ag…

math.OC2022★ 4 cited

DISH: A Distributed Hybrid Primal-Dual Optimization Framework to Utilize System Heterogeneity

Xiaochun Niu, Ermin Wei

We consider solving distributed consensus optimization problems over multi-agent networks. Current distributed methods fail to capture the heterogeneity among agents' local computa…

math.OC2021★ 31 cited

FedHybrid: A Hybrid Primal-Dual Algorithm Framework for Federated Optimization

Xiaochun Niu, Ermin Wei

We consider a multi-agent consensus optimization problem over a server-client (federated) network, where all clients are connected to a central server. Current distributed algorith…