mlsanityData to 14 Oct 2026

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

arXiv 2410.13602 ↗First posted 17 Oct 2024cs.NI cs.LGcited by 4

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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.

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

18 of 48 references.

References · 18

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  6. 2019On the Efficacy of Knowledge Distillationcited by 381
  7. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
  8. 2019Correlation Congruence for Knowledge Distillationcited by 285
  9. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  10. 2020FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measurecited by 46
  11. 2015DeepSat - A Learning framework for Satellite Imagerycited by 38
  12. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
6 more
  1. 2023Why Batch Normalization Damage Federated Learning on Non-IID Data?cited by 29
  2. 2022Federated Learning in Satellite Constellationscited by 21
  3. 2020Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellationscited by 12
  4. 2023FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigationcited by 8
  5. 2023Contrastive encoder pre-training-based clustered federated learning for heterogeneous datacited by 5
  6. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagerycited by 0
  2. 2026A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing Systemcited by 0
  3. 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
  4. 2026FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
  2. 2026FractalNet-Based Heterogeneous Federated Learning for Orbital Edge Intelligence in Satellite Mega-Constellations: A Wildfire Case Study
  3. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes
  4. 2026Resource-Aware Federated Mixture-of-Experts with Adaptive Pruning for Onboard Learning in LEO Satellite Constellations
  5. 2025OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers
  6. 2024Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things
  7. 2025Clustered Federated Learning with Hierarchical Knowledge Distillation
  8. 2023Wireless Federated k-Means Clustering with Non-coherent Over-the-Air Computation