  Federated learning in satellites offers several advantages. Firstly, it
ensures data privacy and security, as sensitive data remains on the satellites
and is not transmitted to a central location. This is particularly important
when dealing with sensitive or classified information. Secondly, federated
learning allows satellites to collectively learn from a diverse set of data
sources, benefiting from the distributed knowledge across the satellite
network. Lastly, the use of federated learning reduces the communication
bandwidth requirements between satellites and the central server, as only model
updates are exchanged instead of raw data. By leveraging federated learning,
satellites can collaborate and continuously improve their machine learning
models while preserving data privacy and minimizing communication overhead.
This enables the development of more intelligent and efficient satellite
systems for various applications, such as Earth observation, weather
forecasting, and space exploration.
