Heterogeneity: An Open Challenge for Federated On-board Machine Learning

The design of satellite missions is currently undergoing a paradigm shift
from the historical approach of individualised monolithic satellites towards
distributed mission configurations, consisting of multiple small satellites.
With a rapidly growing number of such satellites now deployed in orbit, each
collecting large amounts of data, interest in on-board orbital edge computing
is rising. Federated Learning is a promising distributed computing approach in
this context, allowing multiple satellites to collaborate efficiently in
training on-board machine learning models. Though recent works on the use of
Federated Learning in orbital edge computing have focused largely on
homogeneous satellite constellations, Federated Learning could also be employed
to allow heterogeneous satellites to form ad-hoc collaborations, e.g. in the
case of communications satellites operated by different providers. Such an
application presents additional challenges to the Federated Learning paradigm,
arising largely from the heterogeneity of such a system. In this position
paper, we offer a systematic review of these challenges in the context of the
cross-provider use case, giving a brief overview of the state-of-the-art for
each, and providing an entry point for deeper exploration of each issue.