arXiv AI

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

The paper introduces OrbitALIF, a federated learning framework that performs cloud removal on low‑earth‑orbit satellites. It uses a compact 2.30 M‑parameter spiking neural network with adaptive gated fusion and spectral‑spatial hybrid attention modules, enabling both training and inference onboard. The approach achieves competitive cloud‑removal quality while consuming only 0.287 mJ per inference on neuromorphic hardware, a 72.3‑fold energy reduction compared to an equivalent ANN.

arXiv AI

2d ago

FractalNet-Based Heterogeneous Federated Learning for Orbital Edge Intelligence in Satellite Mega-Constellations: A Wildfire Case Study

The paper introduces a heterogeneous federated learning approach using the FractalNet architecture tailored for satellite mega‑constellations. It formalizes contact‑window‑constrained, depth‑heterogeneous optimization and proposes a distributed path scheduler that assigns model depth based on satellite SWAP‑C constraints, predicted contacts, and training statistics. The framework includes periodic update pooling and a three‑tier agentic control plane, and is validated through a wildfire detection case study across LEO, MEO, and GEO/HEO shells, demonstrating improvements in convergence, communication efficiency, energy adaptation, and robustness.

By Sai Puppala, Koushik Sinha

agents

Hugging Face Trending Papers

Jun 4

ATT-CR: Adaptive Triangular Transformer for Cloud Removal

Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing Transformer-based methods utilizing self-attention have shown impressive results by effectively modeling long-range dependencies in cloudy images.

llms benchmarks

Hugging Face Trending Papers

3d ago

FractalNet-Based Heterogeneous Federated Learning for Orbital Edge Intelligence in Satellite Mega-Constellations: A Wildfire Case Study

Satellite mega-constellations are emerging as large-scale sensing, communication, and computation fabrics, yet their learning architectures remain largely inherited from terrestrial federated learning...

agents

Hugging Face Trending Papers

Aug 25

SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning

Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereby avoiding large-scale data downloads to ground se...

benchmarks

arXiv Machine Learning

Aug 11

FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations

arXiv:2608. 09687v1 Announce Type: new Abstract: Federated learning (FL) in Low Earth Orbit (LEO) satellite constellations is affected by non-IID data and irregular ground-station visibility, both driven by orbital geometry.

By Satwat Bashir, Tasos Dagiuklas, Muddesar Iqbal

benchmarks

arXiv AI

Jun 6

ATT-CR: Adaptive Triangular Transformer for Cloud Removal

arXiv:2606. 05999v1 Announce Type: cross Abstract: Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images.

By Yang Wu, Ye Deng, Pengna Li, Wenli Huang, Kangyi Wu, Xiaomeng Xin, Jinjun Wang

llms benchmarks

arXiv Machine Learning

Aug 26

SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning

arXiv:2608.24516v1 Announce Type: cross Abstract: Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, there...

By Hao Wu, Kin Whye Chew, Yizhan Han, Han Li, Jingxian Wang

benchmarks

arXiv Machine Learning

Jul 21

Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction

arXiv:2411. 10921v2 Announce Type: replace Abstract: Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems.

By Maneesha Perera, Julian De Hoog, Kasun Bandara, Hansani Weeratunge, Saman Halgamuge

computer-vision

arXiv Machine Learning

Aug 26

Low-Latency Activation-Regularized Sparse Neural Operators with Distillation Assistance Towards Real-Time Edge-Deployable Virtual Sensing

The paper introduces the Sparse-Activation-ReLU (SAR) layer, a single‑step neural operator that promotes activation sparsity without surrogate‑gradient training and is compatible with event‑based computing. In a trunk‑based NOMAD architecture, SAR improves the combined Latency‑Error‑Energy (LEE) metric by over fivefold compared to Variable Spiking Neuron (VSN) and Leaky Integrate‑and‑Fire (LIF) models. Additional techniques such as synthetic knowledge distillation, a ReLU‑based spiking loss, and graph‑neighbor thresholding further reduce LEE and L2 error on the Heat Exchanger dataset, advancing energy‑efficient virtual sensing for edge deployment.

By William Howes, Farid Ahmed, Syed Bahauddin Alam

efficiency safety

arXiv Computer Vision

Aug 21

Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer

arXiv:2608. 19238v1 Announce Type: cross Abstract: Spiking Transformers model token interactions primarily through spiking self-attention (SSA).

By Dongcheng Zhao, Sicheng Shen, Zhenyu Yang, Zhiyuan Li, Jinyan Yu, Yongjian Wang, Tiechui Yao, Wenli Zhang, Tielin Zhang

llms computer-vision benchmarks

arXiv Machine Learning

Aug 21

Far from the Crowd: Scalable Self-Supervised Learning via Geographic Isolation

arXiv:2608. 19766v1 Announce Type: cross Abstract: Self-supervised pretraining on remote sensing imagery typically treats all samples as equally informative, despite large variability in geographic and visual structure.

By Daniele Rege Cambrin, Francesco Rossi, Mattia Varile

rag benchmarks

arXiv Machine Learning

Jul 13

A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design

arXiv:2607. 09084v1 Announce Type: new Abstract: The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.

By Linhui Xiao, Guiping Cao, Mingyue Guo, Xianchao Guan, Fan Yang, Ming Tao, Xin Li, Yuxin Peng, Yaowei Wang

fine-tuning

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