Details

Autor(en) / Beteiligte

• Jabbarpour, Mohammad Reza
• Javadi, Bahman
• Leong, Philip H.W.
• Calheiros, Rodrigo N.
• Boland, David
• Butler, Chris

Titel

On-Board Federated Learning in Orbital Edge Computing

Ist Teil von

• 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), 2023, p.1045-1052

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Low Earth Orbit (LEO) satellite constellations are used for a wide range of applications including earth observation, communication services, navigation, and positioning. They have emerged as a new source of data but transferring this data to a ground station (GS) for analysis and machine learning requires extensive bandwidth and incurs high latency. Limited battery capacity, communication and computing capabilities are other factors affecting the training process. Federated Learning (FL) is being used to address these challenges, although it heavily relies on the GS for model aggregation. In this paper, we consider Orbital Edge Computing (OEC) as an architecture for LEO satellite constellations and propose an on-board Federated Learning to reduce communication with the GS. We present a novel decentralised FL algorithm, called FedOrbit, based on reinforcement learning cluster formation and satellite visiting patterns to utilise intra and inter-satellite communications for model aggregation. Extensive performance evaluation under Walker Delta-based LEO constellation configurations and different datasets including MNIST, CIFAR-10, and EuroSat revealed that FedOrbit can significantly reduce communication rounds, power consumption and training time in comparison to state-of-the-art FL approaches while maintaining a high accuracy. FedOrbit demonstrates a significant decrease in power consumption, specifically by 8.8% and 79.1% for the MNIST dataset, when compared to decentralised and centralised FL approaches, respectively. The proposed technique can also reduce the training time by 5× and 48× compared with the decentralised and centralised FL approaches, respectively.