Name
Abstract | ||
|---|---|---|
FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-preserving distributed training in wireless edge networks, where edge devices collaboratively train machine learning (ML) models with the orchestration of a server. However, due to frequent communication, FEEL needs to be adapted to the limited communication bandwidth. Furthermore, the statistical heterogeneity of local ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LCN52139.2021.9524974 | 2021 IEEE 46th Conference on Local Computer Networks (LCN) |
Keywords | DocType | ISSN |
Training,Wireless communication,Uncertainty,Processor scheduling,Machine learning,Channel allocation,Scheduling | Conference | 0742-1303 |
ISBN | Citations | PageRank |
978-1-6654-1886-7 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Afaf Taïk | 1 | 0 | 0.34 |
| Hajar Moudoud | 2 | 0 | 0.34 |
| Soumaya Cherkaoui | 3 | 187 | 40.89 |