Distributed Machine Learning via Sufficient Factor Broadcasting
This addresses communication bottlenecks in distributed ML for applications like computer vision and computational biology, offering a significant but incremental improvement over existing methods.
The paper tackles the high parameter synchronization costs in distributed machine learning for large-scale matrix-parameterized models by proposing Sufficient Factor Broadcasting (SFB), which reduces communication costs from quadratic to linear in matrix dimensions, achieving up to 10x speedup in experiments.
Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology. When these models are applied to large-scale ML problems starting at millions of samples and tens of thousands of classes, their parameter matrix can grow at an unexpected rate, resulting in high parameter synchronization costs that greatly slow down distributed learning. To address this issue, we propose a Sufficient Factor Broadcasting (SFB) computation model for efficient distributed learning of a large family of matrix-parameterized models, which share the following property: the parameter update computed on each data sample is a rank-1 matrix, i.e., the outer product of two "sufficient factors" (SFs). By broadcasting the SFs among worker machines and reconstructing the update matrices locally at each worker, SFB improves communication efficiency --- communication costs are linear in the parameter matrix's dimensions, rather than quadratic --- without affecting computational correctness. We present a theoretical convergence analysis of SFB, and empirically corroborate its efficiency on four different matrix-parametrized ML models.