  Distributed statistical learning has become a popular technique for
large-scale data analysis. Most existing work in this area focuses on dividing
the observations, but we propose a new algorithm, DDAC-SpAM, which divides the
features under a high-dimensional sparse additive model. Our approach involves
three steps: divide, decorrelate, and conquer. The decorrelation operation
enables each local estimator to recover the sparsity pattern for each additive
component without imposing strict constraints on the correlation structure
among variables. The effectiveness and efficiency of the proposed algorithm are
demonstrated through theoretical analysis and empirical results on both
synthetic and real data. The theoretical results include both the consistent
sparsity pattern recovery as well as statistical inference for each additive
functional component. Our approach provides a practical solution for fitting
sparse additive models, with promising applications in a wide range of domains.
