Name
Abstract | ||
|---|---|---|
Federated Edge Learning (FEEL) involves the collaborative training of machine learning models among edge devices, with the orchestration of a server in a wireless edge network. Due to frequent model updates, FEEL needs to be adapted to the limited communication bandwidth, scarce energy of edge devices, and the statistical heterogeneity of edge devices’ data distributions. Therefore, a careful sche... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCCN.2021.3100574 | IEEE Transactions on Cognitive Communications and Networking |
Keywords | DocType | Volume |
Training,Computational modeling,Servers,Scheduling,Data models,Wireless communication,Adaptation models | Journal | 8 |
Issue | ISSN | Citations |
1 | 2332-7731 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Afaf Taik | 1 | 1 | 0.35 |
| Zoubeir Mlika | 2 | 13 | 3.84 |
| Soumaya Cherkaoui | 3 | 187 | 40.89 |