An efficient distributed learning algorithm based on effective local functional approximations

12 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Communication Efficient Decentralized Training with Multiple Local Updates (2019, background): “An orthogonal one is to pay more local computation for less communication, e.g., one-shot aggregation [53, 54, 16, 21, 46], primal-dual algorithms [37, 38, 7] and distributed Newton methods [33, 52, 29, 35, 25].”
Stochastic, Distributed and Federated Optimization for Machine Learning (2017, methodology): “A quite similar approach was proposed in [109], with richer class class of subproblems that can be formulated locally, and solved approximately.”
Do Subsampled Newton Methods Work for High-Dimensional Data? (2019, background): “…heavy computation in each iteration and converge in a small number of iterations, they have been adapted to solve distributed machine learning aiming at reducing the communication cost (Shamir, Srebro, and Zhang 2014; Mahajan et al. 2015; Zhang and Lin 2015; Reddi et al. 2016; Wang et al. 2018).”
A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression (2019, methodology): “In recent years, many communication-efﬁcient methods, e.g., (Mahajan et al. 2013; Zhang, Duchi, and Wainwright 2013; Shamir, Srebro, and Zhang 2014; Smith et al. 2016; Wang et al. 2018), have been proposed to make distributed computing more efﬁcient.”