Name
Abstract | ||
|---|---|---|
The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrall... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JSAC.2021.3118346 | IEEE Journal on Selected Areas in Communications |
Keywords | DocType | Volume |
Distance learning,Computer aided instruction,Wireless networks,Training,Data models,Performance evaluation,Measurement | Journal | 39 |
Issue | ISSN | Citations |
12 | 0733-8716 | 12 |
PageRank | References | Authors |
0.56 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingzhe Chen | 1 | 595 | 44.32 |
| Deniz Gündüz | 2 | 1649 | 129.08 |
| Kaibin Huang | 3 | 3155 | 182.06 |
| Walid Saad | 4 | 4450 | 279.64 |
| Mehdi Bennis | 5 | 3652 | 217.26 |
| Aneta Vulgarakis Feljan | 6 | 14 | 3.67 |
| H. V. Poor | 7 | 25411 | 1951.66 |