Report
Sparse Incremental Aggregation in Satellite Federated Learning
| العنوان: | Sparse Incremental Aggregation in Satellite Federated Learning |
|---|---|
| المؤلفون: | Razmi, Nasrin, Mukherjee, Sourav, Matthiesen, Bho, Dekorsy, Armin, Popovski, Petar |
| سنة النشر: | 2025 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | 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. Comment: This paper has been accepted for the 14th International ITG Conference on Systems, Communications and Coding (SCC 2025) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2501.11385 |
| رقم الانضمام: | edsarx.2501.11385 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |