# LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networks

arXiv: https://arxiv.org/abs/2501.01293 · HTML: https://mlsanity.com/paper/2501.01293/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 68 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning](https://mlsanity.com/paper/2205.07246/) (2022)
- [Federated Learning with Partial Model Personalization](https://mlsanity.com/paper/2204.03809/) (2022)
- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Split Learning in 6G Edge Networks](https://mlsanity.com/paper/2306.12194/) (2023)
- [AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks](https://mlsanity.com/paper/2403.13101/) (2024)
- [Revisiting Weighted Aggregation in Federated Learning with Neural Networks](https://mlsanity.com/paper/2302.10911/) (2023)
- [Mitigating Bias in Federated Learning](https://mlsanity.com/paper/2012.02447/) (2020)
- [Hierarchical Split Federated Learning: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2412.07197/) (2024)
- [FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations](https://mlsanity.com/paper/2202.01267/) (2022)
- [FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs](https://mlsanity.com/paper/2205.07216/) (2022)
- [SplitGP: Achieving Both Generalization and Personalization in Federated Learning](https://mlsanity.com/paper/2212.08343/) (2022)
- [SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing](https://mlsanity.com/paper/2405.15542/) (2024)
- [Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge Computing](https://mlsanity.com/paper/2212.11751/) (2022)
- [Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM](https://mlsanity.com/paper/2401.00685/) (2024)
- [S4: Self-Supervised Sensing Across the Spectrum](https://mlsanity.com/paper/2405.01656/) (2024)

## Built on by (most cited first, 22 of 22)

- [HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models](https://mlsanity.com/paper/2505.02795/) (2025)
- [HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems](https://mlsanity.com/paper/2506.08426/) (2025)
- [Multimodal Multi-Agent Empowered Legal Judgment Prediction](https://mlsanity.com/paper/2601.12815/) (2026)
- [State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning](https://mlsanity.com/paper/2503.20613/) (2025)
- [LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems](https://mlsanity.com/paper/2511.19368/) (2025)
- [Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks](https://mlsanity.com/paper/2503.20844/) (2025)
- [Rethinking Membership Inference Attacks Against Transfer Learning](https://mlsanity.com/paper/2501.11577/) (2025)
- [SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression](https://mlsanity.com/paper/2508.12984/) (2025)
- [LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.14680/) (2025)
- [Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generation](https://mlsanity.com/paper/2506.10685/) (2025)
- [A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation](https://mlsanity.com/paper/2507.05731/) (2025)
- [Sample Efficient Experience Replay in Non-stationary Environments](https://mlsanity.com/paper/2509.15032/) (2025)
- [Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge](https://mlsanity.com/paper/2504.03718/) (2025)
- [SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework](https://mlsanity.com/paper/2504.13479/) (2025)
- [Secure Resource Allocation via Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2501.11557/) (2025)
- [ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning](https://mlsanity.com/paper/2603.04437/) (2026)
- [Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification](https://mlsanity.com/paper/2604.14643/) (2026)
- [DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection](https://mlsanity.com/paper/2505.06860/) (2025)
- [SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacy](https://mlsanity.com/paper/2602.04994/) (2026)
- [GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems](https://mlsanity.com/paper/2603.18540/) (2026)
- [Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2606.15625/) (2026)
- [Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank](https://mlsanity.com/paper/2608.16681/) (2026)

## Abstract (arXiv)

Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in deep learning (DL). However, the intermittent connectivity between LEO satellites and ground station (GS) significantly hinders the timely transmission of raw data to GS for centralized learning, while the scaled-up DL models hamper distributed learning on resource-constrained LEO satellites. Though split learning (SL) can be a potential solution to these problems by partitioning a model and offloading primary training workload to GS, the labor-intensive labeling process remains an obstacle, with intermittent connectivity and data heterogeneity being other challenges. In this paper, we propose LEO-Split, a semi-supervised (SS) SL design tailored for satellite networks to combat these challenges. Leveraging SS learning to handle (labeled) data scarcity, we construct an auxiliary model to tackle the training failure of the satellite-GS non-contact time. Moreover, we propose a pseudo-labeling algorithm to rectify data imbalances across satellites. Lastly, an adaptive activation interpolation scheme is devised to prevent the overfitting of server-side sub-model training at GS. Extensive experiments with real-world LEO satellite traces (e.g., Starlink) demonstrate that our LEO-Split framework achieves superior performance compared to state-ofthe-art benchmarks.