Enhancing Privacy via Hierarchical Federated Learning
By Aidmar Wainakh, Alejandro Sánchez Guinea, Tim Grube, Max Mühlhäuser. 2020
57 citations recorded by the source corpus.
Federated learning suffers from several privacy-related issues that expose the participants to various threats. A number of these issues are aggravated by the centralized architecture of federated learning. In this paper, we discuss applying federated learning on a hierarchical architecture as a potential solution. We introduce the opportunities for more flexible decentralized control over the training process and its impact on the participants' privacy. Furthermore, we investigate possibilities to enhance the efficiency and effectiveness of defense and verification methods.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yang Liu
- Qiang Yang
- Abhishek Singh
- Chaoyang He
- Praneeth Vepakomma
- Murali Annavaram
- Jinhyun So
- Ramesh Raskar
Citations in the corpus
- FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “As demonstrated in Figure 3, FL has various topology definitions, such as vertical FL [34, 35, 36, 37, 38, 39, 40], split learning [41, 42], decentralized FL [43, 44, 45, 46], and Hierarchical FL [47, 48, 49, 50, 51, 52].”