Group-Based Alternating Direction Method of Multipliers for Distributed Linear Classification

By Huihui Wang, Yang Gao, Yinghuan Shi, Ruili Wang. 2017

45 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Signal Processing

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Jihong Park
2. Medhi Bennis
3. Anis Elgabli
4. Vaneet Aggarwal
5. Amrit Singh Bedi
6. Merouane Debbah
7. Sumudu Samarakoon
8. Walid Saad

In Signal Processing

1. Medhi Bennis
2. Jihong Park

Citations in the corpus

• GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning (2019, methodology): “Notably, the algorithm in (Wang et al., 2017) still relies on multiple central entities, i.e., master workers under a master-slave architecture, whereas GADMM requires no central entity wherein workers are equally divided into head and tail groups.”
• Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning (2019, unknown): “Notably, the algorithm in [50] still relies on multiple central entities, i.”