| Full text | |
| Author(s): |
Trindade, Silvana
;
da Fonseca, Nelson L. S.
Total Authors: 2
|
| Document type: | Journal article |
| Source: | IEEE INTERNET OF THINGS JOURNAL; v. 11, n. 17, p. 16-pg., 2024-09-01. |
| Abstract | |
Federated learning (FL) is a promising technique for providing distributed learning without clients disclosing their private data. In hierarchical FL (HFL), edge servers partially aggregate the parameters of their connected clients' models, improving scalability and reducing computational overhead on the central server. To speed up the convergence of the global model, only those clients with potential contributions to the model performance will participate in the model training. This article introduces a two-step client selection approach for the HFL and three novel algorithms, which consider a large set of features in this selection and the client's contributions to the model performance. Compared to the selected baseline algorithms, the proposed client selection algorithms reduce CPU utilization by more than 50%, memory usage by 80%, and energy consumption by 50%. (AU) | |
| FAPESP's process: | 23/00673-7 - Distributed intelligence in communications networks and in the internet of things |
| Grantee: | Nelson Luis Saldanha da Fonseca |
| Support Opportunities: | Research Projects - Thematic Grants |