FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations

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.

Interpret-able feedback for AutoML systems

Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts. A shortcoming of these systems is that when they fail to produce a model with high accuracy, the user has no path to improve the model other than hiring a data scientist or learning ML -- this defeats the purpose of AutoML and limits its adoption. We introduce an interpretable data feedback solution for AutoML. Our solution suggests new data points for the user to label (without requiring a pool of unlabeled data) to improve the model's accuracy. Our solution analyzes how features influence the prediction among all ML models in an AutoML ensemble, and we suggest more data samples from feature ranges that have high variance in such analysis. Our evaluation shows that our solution can improve the accuracy of AutoML by 7-8% and significantly outperforms popular active learning solutions in data efficiency, all the while providing the added benefit of being interpretable.