  We focus on a class of non-smooth optimization problems over the Stiefel
manifold in the decentralized setting, where a connected network of $n$ agents
cooperatively minimize a finite-sum objective function with each component
being weakly convex in the ambient Euclidean space. Such optimization problems,
albeit frequently encountered in applications, are quite challenging due to
their non-smoothness and non-convexity. To tackle them, we propose an iterative
method called the decentralized Riemannian subgradient method (DRSM). The
global convergence and an iteration complexity of $\mathcal{O}(\varepsilon^{-2}
\log^2(\varepsilon^{-1}))$ for forcing a natural stationarity measure below
$\varepsilon$ are established via the powerful tool of proximal smoothness from
variational analysis, which could be of independent interest. Besides, we show
the local linear convergence of the DRSM using geometrically diminishing
stepsizes when the problem at hand further possesses a sharpness property.
Numerical experiments are conducted to corroborate our theoretical findings.
