A Communication Efficient Collaborative Learning Framework for Distributed Features

By Yang Liu, Kang Yan, Xinwei Zhang, Liping Li, Cheng Yong, Tianjian Chen, Mingyi Hong, Qiang Yang. 2019

62 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. Chaoyang He
2. Yang Liu
3. Murali Annavaram
4. Vivek Sharma
5. Qiang Yang
6. Abhishek Singh
7. Qinbin Li
8. Xinle Liang

Citations in the corpus

• SplitNN-driven Vertical Partitioning (2020, methodology): “Other lines of work try to avoid these challenges (Liu et al., 2020) following the same design principles as (McMahan et al.”
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020, positive): “As illustrated in Figure 4(c), VFL is the process of aggregating different features and computing the training loss and gradients in a privacy-preserving manner to build a model with data from all parties collaboratively [121, 35, 122, 123].”
• Self-supervised Cross-silo Federated Neural Architecture Search (2021, methodology): “First, a few studies [17], [25], [26] have shown that, when processing complex data (for example, the high-resolution medical images), the communication efficiency of a Federated NAS framework can be prohibitively low.”