  Decentralized stochastic optimization has recently benefited from gradient
tracking methods \cite{DSGT_Pu,DSGT_Xin} providing efficient solutions for
large-scale empirical risk minimization problems. In Part I \cite{GT_SAGA} of
this work, we develop \textbf{\texttt{GT-SAGA}} that is based on a
decentralized implementation of SAGA \cite{SAGA} using gradient tracking and
discuss regimes of practical interest where \textbf{\texttt{GT-SAGA}}
outperforms existing decentralized approaches in terms of the total number of
local gradient computations. In this paper, we describe
\textbf{\texttt{GT-SVRG}} that develops a decentralized gradient tracking based
implementation of SVRG \cite{SVRG}, another well-known variance-reduction
technique. We show that the convergence rate of \textbf{\texttt{GT-SVRG}}
matches that of \textbf{\texttt{GT-SAGA}} for smooth and strongly-convex
functions and highlight different trade-offs between the two algorithms in
various settings.
