FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
This addresses the problem of efficient machine learning for satellite data processing, enabling real-time applications like disaster navigation, though it is incremental as it builds on existing federated learning techniques.
The paper tackles the challenge of training machine learning models on satellite data by proposing FedSpace, a federated learning framework that dynamically schedules model aggregation based on satellite connectivity, reducing training time by 1.7 days (38.6%) compared to state-of-the-art methods.
Large-scale deployments of low Earth orbit (LEO) satellites collect massive amount of Earth imageries and sensor data, which can empower machine learning (ML) to address global challenges such as real-time disaster navigation and mitigation. However, it is often infeasible to download all the high-resolution images and train these ML models on the ground because of limited downlink bandwidth, sparse connectivity, and regularization constraints on the imagery resolution. To address these challenges, we leverage Federated Learning (FL), where ground stations and satellites collaboratively train a global ML model without sharing the captured images on the satellites. We show fundamental challenges in applying existing FL algorithms among satellites and ground stations, and we formulate an optimization problem which captures a unique trade-off between staleness and idleness. We propose a novel FL framework, named FedSpace, which dynamically schedules model aggregation based on the deterministic and time-varying connectivity according to satellite orbits. Extensive numerical evaluations based on real-world satellite images and satellite networks show that FedSpace reduces the training time by 1.7 days (38.6%) over the state-of-the-art FL algorithms.