We study the federated optimization problem from a dual perspective and propose a new algorithm termed federated dual coordinate descent (FedDCD), which is based on a type of coordinate descent method developed by Necora et al.[Journal of Optimization Theory and Applications, 2017]. Additionally, we enhance the FedDCD method with inexact gradient oracles and Nesterov's acceleration. We demonstrate theoretically that our proposed approach achieves better convergence rates than the state-of-the-art primal federated optimization algorithms under certain situations. Numerical experiments on real-world datasets support our analysis.
@article{arxiv.2201.11183,
title = {A dual approach for federated learning},
author = {Zhenan Fan and Huang Fang and Michael P. Friedlander},
journal= {arXiv preprint arXiv:2201.11183},
year = {2022}
}