# SCOPE: Scalable Composite Optimization for Learning on Spark

arXiv: https://arxiv.org/abs/1602.00133 · HTML: https://mlsanity.com/paper/1602.00133/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2016.

## Built on by (most cited first, 2 of 2)

- [FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning](https://mlsanity.com/paper/2303.05206/) (2023)
- [On the Optimal Batch Size for Byzantine-Robust Distributed Learning](https://mlsanity.com/paper/2305.13856/) (2023)

## Abstract (arXiv)

Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better performance than traditional batch methods. However, most of these DSO methods are not scalable enough. In this paper, we propose a novel DSO method, called underlinescalable underlinecomposite underlineoptimization for lunderlineearning~(SCOPE), and implement it on the fault-tolerant distributed platform mboxSpark. SCOPE is both computation-efficient and communication-efficient. Theoretical analysis shows that SCOPE is convergent with linear convergence rate when the objective function is convex. Furthermore, empirical results on real datasets show that SCOPE can outperform other state-of-the-art distributed learning methods on Spark, including both batch learning methods and DSO methods.