Title
Distributed Machine Learning via Sufficient Factor Broadcasting.
Abstract
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.
Year
Venue
Field
2015
CoRR
Convergence (routing),Outer product,Broadcasting,Synchronization,Matrix (mathematics),Computer science,Correctness,Quadratic equation,Theoretical computer science,Artificial intelligence,Machine learning,Computation
DocType
Volume
Citations 
Journal
abs/1511.08486
3
PageRank 
References 
Authors
0.41
21
7
Name
Order
Citations
PageRank
Pengtao Xie133922.63
Jin Kyu Kim243417.53
Yi Zhou36517.55
Ho, Qirong463630.75
Abhimanu Kumar52279.76
Yaoliang Yu666934.33
Bo Xing77332471.43