Name
Abstract | ||
|---|---|---|
Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally. The non-independent-and-identically-distributed (non-IID) data samples across participating nodes slow model training and impose additional communication rounds for FL to converge. In this paper, we propose... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCCN.2021.3084406 | IEEE Transactions on Cognitive Communications and Networking |
Keywords | DocType | Volume |
Data models,Training,Convergence,Adaptation models,Collaborative work,Distributed databases,Servers | Journal | 7 |
Issue | ISSN | Citations |
4 | 2332-7731 | 3 |
PageRank | References | Authors |
0.43 | 0 | 2 |