  Satellite technologies have advanced drastically in recent years, leading to
a heated interest in launching small satellites into low Earth orbit (LEOs) to
collect massive data such as satellite imagery. Downloading these data to a
ground station (GS) to perform centralized learning to build an AI model is not
practical due to the limited and expensive bandwidth. Federated learning (FL)
offers a potential solution but will incur a very large convergence delay due
to the highly sporadic and irregular connectivity between LEO satellites and
GS. In addition, there are significant security and privacy risks where
eavesdroppers or curious servers/satellites may infer raw data from satellites'
model parameters transmitted over insecure communication channels. To address
these issues, this paper proposes FedSecure, a secure FL approach designed for
LEO constellations, which consists of two novel components: (1) decentralized
key generation that protects satellite data privacy using a functional
encryption scheme, and (2) on-orbit model forwarding and aggregation that
generates a partial global model per orbit to minimize the idle waiting time
for invisible satellites to enter the visible zone of the GS. Our analysis and
results show that FedSecure preserves the privacy of each satellite's data
against eavesdroppers, a curious server, or curious satellites. It is
lightweight with significantly lower communication and computation overheads
than other privacy-preserving FL aggregation approaches. It also reduces
convergence delay drastically from days to only a few hours, yet achieving high
accuracy of up to 85.35% using realistic satellite images.
