Federated learning for LEO constellations via inter-HAP links
- URL: http://arxiv.org/abs/2205.07216v2
- Date: Tue, 17 May 2022 03:17:40 GMT
- Title: Federated learning for LEO constellations via inter-HAP links
- Authors: Mohamed Elmahallawy, Tony Luo
- Abstract summary: Low Earth Obit (LEO) satellite constellations have seen a sharp increase of deployment in recent years.
To apply machine learning (ML) in such applications, the traditional way of downloading satellite data such as imagery to a ground station (GS) is not desirable.
We show that existing FL solutions do not fit well in such LEO constellation scenarios because of significant challenges such as excessive convergence delay and unreliable wireless channels.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Low Earth Obit (LEO) satellite constellations have seen a sharp increase of
deployment in recent years, due to their distinctive capabilities of providing
broadband Internet access and enabling global data acquisition as well as
large-scale AI applications. To apply machine learning (ML) in such
applications, the traditional way of downloading satellite data such as imagery
to a ground station (GS) and then training a model in a centralized manner, is
not desirable because of the limited bandwidth, intermittent connectivity
between satellites and the GS, and privacy concerns on transmitting raw data.
Federated Learning (FL) as an emerging communication and computing paradigm
provides a potentially supreme solution to this problem. However, we show that
existing FL solutions do not fit well in such LEO constellation scenarios
because of significant challenges such as excessive convergence delay and
unreliable wireless channels. To this end, we propose to introduce
high-altitude platforms (HAPs) as distributed parameter servers (PSs) and
propose a synchronous FL algorithm, FedHAP, to accomplish model training in an
efficient manner via inter-satellite collaboration. To accelerate convergence,
we also propose a layered communication scheme between satellites and HAPs that
FedHAP leverages. Our simulations demonstrate that FedHAP attains model
convergence in much fewer communication rounds than benchmarks, cutting the
training time substantially from several days down to a few hours with the same
level of resulting accuracy.
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