  Federated learning (FL) is a distributed learning paradigm that allows
several clients to learn a global model without sharing their private data. In
this paper, we generalize a primal dual fixed point (PDFP) \cite{PDFP} method
to federated learning setting and propose an algorithm called Federated PDFP
(FPDFP) for solving composite optimization problems. In addition, a
quantization scheme is applied to reduce the communication overhead during the
learning process. An $O(\frac{1}{k})$ convergence rate (where $k$ is the
communication round) of the proposed FPDFP is provided. Numerical experiments,
including graph-guided logistic regression, 3D Computed Tomography (CT)
reconstruction are considered to evaluate the proposed algorithm.
