On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System

By Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing. 2016

30 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.

1. Eric P. Xing
2. Wei Dai
3. Qirong Ho
4. Yaochen Hu
5. Pengtao Xie
6. Di Niu
7. Peng Liu
8. Linglong Kong

Citations in the corpus

• Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, methodology): “Zhou et al. (2016) proposes a proximal gradient method and mainly focuses on speeding up training in a model-parallel scenario.”
• Strategies and Principles of Distributed Machine Learning on Big Data (2015, methodology): “Taking into account the common ML practice of writing ML programs for application-specific instances, a usable software platform for ML practitioners could instead offer two utilities: 1 a ready-to-run set of ML workhorse implementations—such as stochastic proximal descent algorithms [42,43], coordi”
• FDML: A Collaborative Machine Learning Framework for Distributed Features (2019, background): “Since neither the original features nor the local model parameters of a party are transferred to any external sites, the FDML system preserves data locality and is much less vulnerable to model inversion attacks [12] targeting other collaborative learning algorithms [27, 31] that share model paramet”