3 papers
eess.SY2026
Local Updates in Distributed Optimization: Provable Acceleration and Topology Effects
Zuang Wang, Yongqiang Wang
Inspired by the success of performing multiple local optimization steps between communication rounds in federated learning, incorporating such local updates into distributed optimi…
math.OC2025
Guaranteeing Both Consensus and Optimality in Decentralized Nonconvex Optimization with Multiple Local Updates
Jie Liu, Zuang Wang, Yongqiang Wang
Scalable decentralized optimization in large-scale systems hinges on efficient communication. A common way to reduce communication overhead is to perform multiple local updates bet…
cs.LG2025
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
Jie Liu, Yongqiang Wang
By letting local clients perform multiple local updates before communicating with a parameter server, modern federated learning algorithms such as FedAvg tackle the communication b…