Semantic Scholar Open Access 2019 373 sitasi

Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks

Mehdi Salehi Heydar Abad Emre Ozfatura Deniz Gündüz Özgür Erçetin

Lihat Sumber DOI

Abstrak

We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively learn a global model by sharing local updates on the model parameters rather than their datasets, with the help of a mobile base station (MBS). We optimize the resource allocation among MUs to reduce the communication latency in learning iterations. Observing that the performance in this centralized setting is limited due to the distance of the cell-edge users to the MBS, we introduce small cell base stations (SBSs) orchestrating FEEL among MUs within their cells, and periodically exchanging model updates with the MBS for global consensus. We show that this hierarchical federated learning (HFL) scheme significantly reduces the communication latency without sacrificing the accuracy.

Topik & Kata Kunci

Computer Science Engineering Mathematics

Penulis (4)

Mehdi Salehi Heydar Abad

Emre Ozfatura

Deniz Gündüz

Özgür Erçetin

Format Sitasi

APA MLA BibTeX

Abad, M.S.H., Ozfatura, E., Gündüz, D., Erçetin, Ö. (2019). Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks. https://doi.org/10.1109/ICASSP40776.2020.9054634

Akses Cepat

Lihat di Sumber doi.org/10.1109/ICASSP40776.2020.9054634

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 373×
Sumber Database: Semantic Scholar
DOI: 10.1109/ICASSP40776.2020.9054634
Akses: Open Access ✓