Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
No author records on this work.
The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Practical Secure Aggregation for Privacy-Preserving Machine Learning | 2017 | 3,744 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Federated Learning: Challenges, Methods, and Future Directions | 2020 | 4,992 |
| Federated Learning for Healthcare Informatics | 2020 | 1,497 |
| A survey on security and privacy of federated learning | 2020 | 1,393 |
Links
Topics
| Privacy-Preserving Technologies in Data | Computer Science |
| Cryptography and Data Security | Computer Science |
| Vehicular Ad Hoc Networks (VANETs) | Engineering |
Is this record sound?
partial
One field of this record is missing or disagrees with another. What is shown below is what the source publishes.
- weakensThe source lists no authors for this work at all, so there is nobody to attribute it to and it appears on no author page.
- supports18 reference(s) recorded.
- supportsThe DOI's year agrees with the publication year.
- supportsA title is present.
Provenance
sha256 b3024609427ad450…