Over-the-Air Federated Learning in Satellite systems

• URL: http://arxiv.org/abs/2306.02996v1
• Date: Mon, 5 Jun 2023 16:06:39 GMT
• Title: Over-the-Air Federated Learning in Satellite systems
• Authors: Edward Akito Carlos, Raphael Pinard, Mitra Hassani
• Abstract summary: Federated learning in satellites offers several advantages. It ensures data privacy and security, as sensitive data remains on the satellites and is not transmitted to a central location. By leveraging federated learning, satellites can collaborate and continuously improve their machine learning models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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