Name
Abstract | ||
|---|---|---|
Machine learning relies on the availability of vast amounts of data for training. However, in reality, data are mostly scattered across different organizations and cannot be easily integrated due to many legal and practical constraints. To address this important challenge in the field of machine learning, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical modeling under a data federation. FTL allows knowledge to be shared without compromising user privacy and enables complementary knowledge to be transferred across domains in a data federation, thereby enabling a target-domain party to build flexible and effective models by leveraging rich labels from a source domain. This framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the nonprivacy-preserving transfer learning. It is flexible and can be effectively adapted to various secure multiparty machine learning tasks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MIS.2020.2988525 | IEEE Intelligent Systems |
Keywords | DocType | Volume |
Federated Learning,Transfer Learning,Multi-party Computation,Secret Sharing,Homomorphic Encryption | Journal | 35 |
Issue | ISSN | Citations |
4 | 1541-1672 | 12 |
PageRank | References | Authors |
0.66 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liu Yang | 1 | 314 | 20.37 |
| Yan Kang | 2 | 12 | 0.66 |
| Chaoping Xing | 3 | 916 | 110.47 |
| Tianjian Chen | 4 | 22 | 1.19 |
| Qiang Yang | 5 | 17039 | 875.69 |