4 research outputs found
Federated Primal Dual Fixed Point Algorithm
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 convergence rate (where 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.Comment: 29 pages and 8 figure