1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices
Y. Li, X. Qin, J. Geng +5
Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing P…
cs.LG2022★ 1 cited
FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity
Yonghai Gong, Yichuan Li, Nikolaos M. Freris
Federated Learning (FL) is an emerging framework for distributed processing of large data volumes by edge devices subject to limited communication bandwidths, heterogeneity in data…
math.OC2021
BFGS-ADMM for Large-Scale Distributed Optimization
Yichuan Li, Yonghai Gong, Nikolaos M. Freris +2
We consider a class of distributed optimization problem where the objective function consists of a sum of strongly convex and smooth functions and a (possibly nonsmooth) convex reg…