# Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach

arXiv: https://arxiv.org/abs/2410.13602 · HTML: https://mlsanity.com/paper/2410.13602/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [On the Efficacy of Knowledge Distillation](https://mlsanity.com/paper/1910.01348/) (2019)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [Correlation Congruence for Knowledge Distillation](https://mlsanity.com/paper/1904.01802/) (2019)
- [FedALA: Adaptive Local Aggregation for Personalized Federated Learning](https://mlsanity.com/paper/2212.01197/) (2022)
- [FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure](https://mlsanity.com/paper/2010.06870/) (2020)
- [DeepSat - A Learning framework for Satellite Imagery](https://mlsanity.com/paper/1509.03602/) (2015)
- [Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2109.01348/) (2021)
- [Why Batch Normalization Damage Federated Learning on Non-IID Data?](https://mlsanity.com/paper/2301.02982/) (2023)
- [Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2206.00307/) (2022)
- [Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellations](https://mlsanity.com/paper/2005.07965/) (2020)
- [FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation](https://mlsanity.com/paper/2304.08537/) (2023)
- [Contrastive encoder pre-training-based clustered federated learning for heterogeneous data](https://mlsanity.com/paper/2311.16535/) (2023)
- [Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning](https://mlsanity.com/paper/2401.15541/) (2024)

## Built on by (most cited first, 4 of 4)

- [Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagery](https://mlsanity.com/paper/2603.07774/) (2026)
- [A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System](https://mlsanity.com/paper/2603.09727/) (2026)
- [On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations](https://mlsanity.com/paper/2604.16518/) (2026)
- [FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations](https://mlsanity.com/paper/2608.03436/) (2026)

## Abstract (arXiv)

Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an effective EOD processing mechanism, it is imperative to investigate: 1) the challenge of processing the observed data without transmitting those large-size data to the ground because the connection between the satellites and the ground stations is intermittent, and 2) the challenge of processing the non-independent and identically distributed (non-IID) satellite data. In this paper, to cope with those challenges, we propose an orbit-based spectral clustering-assisted clustered federated self-knowledge distillation (OSC-FSKD) approach for each orbit of an LEO satellite constellation, which retains the advantage of FL that the observed data does not need to be sent to the ground. Specifically, we introduce normalized Laplacian-based spectral clustering (NLSC) into federated learning (FL) to create clustered FL in each round to address the challenge resulting from non-IID data. Particularly, NLSC is adopted to dynamically group clients into several clusters based on cosine similarities calculated by model updates. In addition, self-knowledge distillation is utilized to construct each local client, where the most recent updated local model is used to guide current local model training. Experiments demonstrate that the observation accuracy obtained by the proposed method is separately 1.01x, 2.15x, 1.10x, and 1.03x higher than that of pFedSD, FedProx, FedAU, and FedALA approaches using the SAT4 dataset. The proposed method also shows superiority when using other datasets.