  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.
