6 papers
Optimal Network Dependence in Distributed Stochastic Optimization via Tree Routing
Runze You, Shi Pu
Communication is a central bottleneck in distributed optimization, but its effect is often summarized by the spectral gap of a chosen mixing matrix. Since this gap depends on link…
FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
Runze You, Shi Pu
Federated Learning (FL) methods are often designed for specific client participation patterns, limiting their applicability in practical deployments. We introduce the FedSUM family…
Stochastic Push-Pull for Decentralized Nonconvex Optimization
Runze You, Shi Pu
To understand the convergence behavior of the Push-Pull method for decentralized optimization with stochastic gradients (Stochastic Push-Pull), this paper presents a comprehensive…
Decentralized Min-Max Optimization with Gradient Tracking
Runze You, Kun Huang, Shi Pu
This paper presents a novel distributed formulation of the min-max optimization problem. Such a formulation enables enhanced flexibility among agents when optimizing their maximiza…
Distributed Learning over Arbitrary Topology: Linear Speed-Up with Polynomial Transient Time
Runze You, Shi Pu
We study a distributed learning problem in which agents, each with potentially heterogeneous local data, collaboratively minimize the sum of their local cost functions via peer…
B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
Runze You, Shi Pu
This paper considers the distributed learning problem where a group of agents cooperatively minimizes the summation of their local cost functions based on peer-to-peer communicatio…