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An efficient distributed learning algorithm based on effective local functional approximations

Abstract: Scalable machine learning over big data is an important problem that is receiving a lot of attention in recent years. On popular distributed environments such as Hadoop running on a cluster of commodity machines, communication costs are substantial and algorithms need to be designed suitably considering those costs. In this paper we give a novel approach to the distributed training of linear classifiers (involving smooth losses and L2 regularization) that is designed to reduce the total communication costs. At each iteration, the nodes minimize locally formed approximate objective functions; then the resulting minimizers are combined to form a descent direction to move. Our approach gives a lot of freedom in the formation of the approximate objective function as well as in the choice of methods to solve them. The method is shown to have O(log(1/ε)) time convergence. The method can be viewed as an iterative parameter mixing method. A special instantiation yields a parallel stochastic gradient descent method with strong convergence. When communication times between nodes are large, our method is much faster than the Terascale method (Agarwal et al., 2011), which is a state of the art distributed solver based on the statistical query model (Chu et al., 2006) that computes function and gradient values in a distributed fashion. We also evaluate against other recent distributed methods and demonstrate superior performance of our method.

Dhruv Mahajan, Nikunj Agrawal, S Sathiya Keerthi, Sundararajan Sellamanickam and Léon Bottou: An efficient distributed learning algorithm based on effective local functional approximations, The Journal of Machine Learning Research, 19(1):2942–2978, 2018.

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@article{mahajan-2018,
  title = {An efficient distributed learning algorithm based on effective local functional approximations},
  author = {Mahajan, Dhruv and Agrawal, Nikunj and Keerthi, S Sathiya and Sellamanickam, Sundararajan and Bottou, L{\'e}on},
  journal = {The Journal of Machine Learning Research},
  volume = {19},
  number = {1},
  pages = {2942--2978},
  year = {2018},
  url = {http://leon.bottou.org/papers/mahajan-2018},
}