Name
Abstract | ||
|---|---|---|
The decentralized nature of federated learning, that often leverages the power of edge devices, makes it vulnerable to attacks against privacy and security. The privacy risk for a peer is that the model update she computes on her private data may, when sent to the model manager, leak information on those private data. Even more obvious are security attacks, whereby one or several malicious peers r... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JIOT.2021.3102155 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Protocols,Collaborative work,Security,Data models,Computational modeling,Privacy,Internet of Things | Journal | 9 |
Issue | ISSN | Citations |
5 | 2327-4662 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Josep Domingo | 1 | 67 | 12.25 |
| Alberto Blanco-Justicia | 2 | 14 | 6.77 |
| Jesús A. Manjón | 3 | 27 | 1.77 |
| David Sánchez | 4 | 690 | 33.01 |