Sparse Federated Representation Learning for planetary geology survey missions in hybrid quantum-classical pipelines
The article discusses the challenges of analyzing hyperspectral imaging data from Mars Reconnaissance Orbiter's CRISM instrument, including extreme communication latency, asymmetric bandwidth, distributed processing, data heterogeneity, and resource constraints. It introduces Sparse Federated Representation Learning (SFRL) as a solution, leveraging sparse coding and quantum optimization.
Why it matters
SFRL addresses the unique challenges of planetary geology surveys, enabling efficient analysis of distributed, heterogeneous data using a hybrid quantum-classical pipeline.
Key Points
- 1Planetary geology surveys face unique constraints like communication latency, bandwidth asymmetry, and data heterogeneity
- 2Sparse coding can extract fundamental geological
- 3 from distributed data without sharing raw samples
- 4Certain sparse coding optimization problems map to Ising models, suggesting quantum processors could accelerate the discovery of optimal basis functions
- 5The convergence of federated learning, sparse representation, and quantum optimization forms the foundation of SFRL for planetary survey missions
Details
The article describes a research collaboration to analyze hyperspectral imaging data from the Mars Reconnaissance Orbiter's CRISM instrument. The challenge was not just the volume of data, but its distribution across three different ground stations with varying computational capabilities and data sovereignty requirements. Traditional centralized learning approaches were infeasible due to transmission constraints. The author discovered that even federated averaging (FedAvg) struggled with the extreme heterogeneity of geological features, leading to catastrophic forgetting in global models. This realization prompted the exploration of sparse coding techniques to extract fundamental geological
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