ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal

• URL: http://arxiv.org/abs/2608.24073v1
• Date: Tue, 25 Aug 2026 05:11:19 GMT
• Title: ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal
• Abstract summary: Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance.<n>Conventional cloud-removal pipelines that download cloudy images to ground stations for processing suffer from limited contact windows, constrained satellite-to-ground bandwidth, and high latency.<n>We propose a novel satellite federated learning framework for cloud removal across constellations, named orbital attention leaky integrate-and-fire (OrbitALIF)
• Score: 46.13568040085732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth's surface, and conventional cloud-removal pipelines that download cloudy images to ground stations for processing suffer from limited contact windows, constrained satellite-to-ground bandwidth, and high latency. In this work, we propose a novel satellite federated learning framework for cloud removal across LEO constellations, named orbital attention leaky integrate-and-fire (OrbitALIF). OrbitALIF performs both onboard training and inference using a compact 2.30,M-parameter spiking neural network (SNN) backbone with an adaptive gated fusion module (AGFM) and a spectral-spatial hybrid attention module (SHAM), combined with a decentralized federated learning strategy that shares model weights via inter-satellite links. Our experiments show that OrbitALIF achieves competitive cloud removal quality while consuming only 0.287,mJ per inference on neuromorphic hardware, a 72.3 times (98.6%) energy reduction versus an equivalent artificial neural network (ANN).

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