# Feature-Distributed SVRG for High-Dimensional Linear Classification

arXiv: https://arxiv.org/abs/1802.03604 · HTML: https://mlsanity.com/paper/1802.03604/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2018.

## Built on by (most cited first, 8 of 8)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating](https://mlsanity.com/paper/2103.00958/) (2021)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Fair and efficient contribution valuation for vertical federated learning](https://mlsanity.com/paper/2201.02658/) (2022)
- [AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization](https://mlsanity.com/paper/2109.12519/) (2021)
- [Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm](https://mlsanity.com/paper/2203.10329/) (2022)
- [Achieving Model Fairness in Vertical Federated Learning](https://mlsanity.com/paper/2109.08344/) (2021)
- [A Vertical Federated Learning Framework for Horizontally Partitioned Labels](https://mlsanity.com/paper/2106.10056/) (2021)

## Abstract (arXiv)

Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to design distributed learning methods on a cluster of multiple machines. In this paper, we propose a new distributed learning method, called feature-distributed stochastic variance reduced gradient (FD-SVRG) for high-dimensional linear classification. Unlike most existing distributed learning methods which are instance-distributed, FD-SVRG is feature-distributed. FD-SVRG has lower communication cost than other instance-distributed methods when the data dimensionality is larger than the number of data instances. Experimental results on real data demonstrate that FD-SVRG can outperform other state-of-the-art distributed methods for high-dimensional linear classification in terms of both communication cost and wall-clock time, when the dimensionality is larger than the number of instances in training data.