Name
Abstract | ||
|---|---|---|
Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. Federated learning can be a promising solution for enabling IoT-based smart applications. In this article, we present the primary design aspects for enabling federated learning at the network edge. We model the incentive- based interaction between a global server and participating devices for federated learning via a Stackelberg game to motivate the participation of the devices in the federated learning process. We present several open research challenges with their possible solutions. Finally, we provide an outlook on future research. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MCOM.001.1900649 | IEEE Communications Magazine |
Keywords | DocType | Volume |
Stackelberg game,resource optimization,network edge,IoT-based smart applications,machine learning,Internet of Things,edge networks,federated learning,global server,incentive mechanism | Journal | 58 |
Issue | ISSN | Citations |
10 | 0163-6804 | 22 |
PageRank | References | Authors |
0.69 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Latif U. Khan | 1 | 29 | 2.86 |
| Shashi Raj Pandey | 2 | 85 | 8.95 |
| Nguyen H. Tran | 3 | 399 | 52.48 |
| Walid Saad | 4 | 4450 | 279.64 |
| Zhu Han | 5 | 11215 | 760.71 |
| Minh N. H. Nguyen | 6 | 121 | 8.25 |
| Choong Seon Hong | 7 | 2044 | 277.88 |