arXiv cs.AI· Sai Puppala, Bhabani P. Sinha, Koushik Sinha·· 3 天前AI 评分20
基于 FractalNet 的异构联邦学习：面向卫星巨型星座轨道边缘智能的野火检测案例研究
FractalNet-Based Heterogeneous Federated Learning for Orbital Edge Intelligence in Satellite Mega-Constellations: A Wildfire Case Study
AI 导读
研究提出一种基于 FractalNet 架构的异构联邦学习方法，用于卫星巨型星座的轨道边缘智能，形式化了受接触窗口约束、深度异构的联邦优化问题，并引入按 SWAP-C 约束、星间接触预测和训练统计分配模型深度的分布式路径调度器。
正文 · 原文
Abstract:Satellite mega-constellations are emerging as large-scale sensing, communication, and computation fabrics, yet their learning architectures remain largely inherited from terrestrial federated learning and ground-centric mission operations--- ill-suited to satellites that differ by orders of magnitude in Size, Weight, Power, and Cost (SWAP-C), radiation tolerance, link availability, and propagation delay. We propose a heterogeneous federated learning method based on the FractalNet architecture for orbital edge intelligence. We formalize contact-window-constrained, depth-heterogeneous federated optimization and introduce a distributed path scheduler that assigns model depth as a function of SWAP-C constraints, predicted inter-satellite contacts, and training statistics. To reduce message overhead and energy consumption, each tier pools updates periodically rather than at every contact opportunity, and a three-tier agentic control plane governs in-space scheduling, anomaly escalation, and policy-governed autonomy. As a case study, we apply the framework to wildfire detection, where each orbital shell naturally learns a different semantic level of situational awareness: pixel-scale thermal anomalies at low Earth orbit (LEO), regional fire-front dynamics at medium Earth orbit (MEO), and larger-scale risk propagation at geostationary or high Earth orbit (GEO/HEO). Experiments on simulated mega-constellations validate the approach across convergence, communication efficiency, energy adaptation, scheduled-pooling savings, robustness, and latency.
| Subjects: | Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.00875 [cs.AI] |
| (or arXiv:2609.00875v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.00875 arXiv-issued DOI via DataCite |
Submission history
From: Koushik Sinha [view email]
[v1]
Tue, 1 Sep 2026 08:07:39 UTC (2,510 KB)
[v2]
Sun, 11 Oct 2026 22:26:10 UTC (2,510 KB)
来源：arXiv cs.AI · arxiv.org