Name
Title | ||
|---|---|---|
Federated Transfer Learning With Client Selection for Intrusion Detection in Mobile Edge Computing |
Abstract | ||
|---|---|---|
In this letter, we propose an efficient federated transfer learning (FTL) framework with client selection for intrusion detection (ID) in mobile edge computing (MEC). Specifically, we leverage federated learning (FL) to preserve privacy by training model locally, and utilize transfer learning (TL) to improve training efficiency by knowledge transfer. For FL, unreliable and low-quality clients shou... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LCOMM.2022.3140273 | IEEE Communications Letters |
Keywords | DocType | Volume |
Training,Servers,Computational modeling,Data models,Transfer learning,Multi-access edge computing,Training data | Journal | 26 |
Issue | ISSN | Citations |
3 | 1089-7798 | 1 |
PageRank | References | Authors |
0.34 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanyu Cheng | 1 | 1 | 0.34 |
| Jianyuan Lu | 2 | 6 | 1.80 |
| Niyato Dusit | 3 | 9486 | 547.06 |
| Biao Lyu | 4 | 1 | 0.68 |
| Jiawen Kang | 5 | 543 | 31.46 |
| Shunmin Zhu | 6 | 6 | 1.80 |