Sparse Incremental Aggregation in Satellite Federated Learning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916572779511808 |
|---|---|
| author | Razmi, Nasrin Mukherjee, Sourav Matthiesen, Bho Dekorsy, Armin Popovski, Petar |
| author_facet | Razmi, Nasrin Mukherjee, Sourav Matthiesen, Bho Dekorsy, Armin Popovski, Petar |
| contents | This paper studies Federated Learning (FL) in low Earth orbit (LEO) satellite constellations, where satellites are connected via intra-orbit inter-satellite links (ISLs) to their neighboring satellites. During the FL training process, satellites in each orbit forward gradients from nearby satellites, which are eventually transferred to the parameter server (PS). To enhance the efficiency of the FL training process, satellites apply in-network aggregation, referred to as incremental aggregation. In this work, the gradient sparsification methods from [1] are applied to satellite scenarios to improve bandwidth efficiency during incremental aggregation. The numerical results highlight an increase of over 4 x in bandwidth efficiency as the number of satellites in the orbital plane increases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11385 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Incremental Aggregation in Satellite Federated Learning Razmi, Nasrin Mukherjee, Sourav Matthiesen, Bho Dekorsy, Armin Popovski, Petar Signal Processing This paper studies Federated Learning (FL) in low Earth orbit (LEO) satellite constellations, where satellites are connected via intra-orbit inter-satellite links (ISLs) to their neighboring satellites. During the FL training process, satellites in each orbit forward gradients from nearby satellites, which are eventually transferred to the parameter server (PS). To enhance the efficiency of the FL training process, satellites apply in-network aggregation, referred to as incremental aggregation. In this work, the gradient sparsification methods from [1] are applied to satellite scenarios to improve bandwidth efficiency during incremental aggregation. The numerical results highlight an increase of over 4 x in bandwidth efficiency as the number of satellites in the orbital plane increases. |
| title | Sparse Incremental Aggregation in Satellite Federated Learning |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2501.11385 |