  Sparse principal component analysis (PCA) improves interpretability of the
classic PCA by introducing sparsity into the dimension-reduction process.
Optimization models for sparse PCA, however, are generally non-convex,
non-smooth and more difficult to solve, especially on large-scale datasets
requiring distributed computation over a wide network. In this paper, we
develop a distributed and centralized algorithm called DSSAL1 for sparse PCA
that aims to achieve low communication overheads by adapting a newly proposed
subspace-splitting strategy to accelerate convergence. Theoretically,
convergence to stationary points is established for DSSAL1. Extensive numerical
results show that DSSAL1 requires far fewer rounds of communication than
state-of-the-art peer methods. In addition, we make the case that since
messages exchanged in DSSAL1 are well-masked, the possibility of private-data
leakage in DSSAL1 is much lower than in some other distributed algorithms.
