CO-OP: Cooperative Machine Learning from Mobile Devices

By Yushi Wang. 2017

32 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management

How researchers see this paper

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

In Artificial Intelligence

1. Qinbin Li
2. Bingsheng He
3. Zhaomin Wu
4. Zeyi Wen
5. Hakima Ould-Slimane
6. Azzam Mourad
7. Mohsen Guizani
8. Chamseddine Talhi

In Computer Vision and Pattern Recognition

1. Hakima Ould-Slimane
2. Bingsheng He
3. Azzam Mourad
4. Mohsen Guizani
5. Chamseddine Talhi
6. Simon Smith
7. Mats Jirstrand
8. Emil Gustavsson

In Information Systems and Management

1. Bingsheng He
2. Simon Smith
3. Mats Jirstrand
4. Emil Gustavsson
5. Gregor Ulm
6. Adrian Nilsson
7. Zeyi Wen
8. Zhaomin Wu

Citations in the corpus

• Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy (2020, neutral): “• CO-OP [15].”
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Three FL algorithms, FedAvg, FedSVRG [87] and CO-OP [180] are compared in both IID and non-IID setup in their work, and the result”
• A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond (2021, background): “FSVRG—federated stochastic variance reduced gradient [51], and CO-OP—cooperative ML [53].”
• A Performance Evaluation of Federated Learning Algorithms (2018, methodology): “Whereas FedAvg and FSVRG rely on synchronized model updates, CO-OP [14] proposes an asynchronous approach.”