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SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework

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arxiv 2409.13503 v3 pith:WVU7XLFV submitted 2024-09-20 cs.DC cs.AIcs.LG

Yuxin Zhang , Zheng Lin , Zhe Chen , Zihan Fang , Wenjun Zhu , Xianhao Chen , Jin Zhao , Yue Gao This is my paper · ORCID
classification cs.DCcs.AIcs.LG
keywords bandwidthheterogeneoussatfednetworkssatellite-assistedterrestrialchallengescommunication
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Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model convergence. Fortunately, the advancement of low-Earth orbit (LEO) satellite networks offers promising new communication avenues to augment traditional terrestrial FL. Despite this potential, the limited satellite-ground communication bandwidth and the heterogeneous operating environments of ground devices-including variations in data, bandwidth, and computing power-pose substantial challenges for effective and robust satellite-assisted FL. To address these challenges, we propose SatFed, a resource-efficient satellite-assisted heterogeneous FL framework. SatFed implements freshness-based model prioritization queues to optimize the use of highly constrained satellite-ground bandwidth, ensuring the transmission of the most critical models. Additionally, a multigraph is constructed to capture real-time heterogeneous relationships between devices, including data distribution, terrestrial bandwidth, and computing capability. This multigraph enables SatFed to aggregate satellite-transmitted models into peer guidance, enhancing local training in heterogeneous environments. Extensive experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared to state-of-the-art benchmarks.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. WAFBOOSTER: Automatic Boosting of WAF Security Against Mutated Malicious Payloads

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  3. Hierarchical Split Federated Learning: Convergence Analysis and System Optimization

    cs.LG 2024-12 conditional novelty 6.0 of 10

    Derives a convergence bound for hierarchical split federated learning and uses it to jointly optimize model-splitting points and per-tier aggregation intervals.

  4. PHandover: Parallel Handover in Mobile Satellite Network

    cs.NI 2025-07 conditional novelty 5.0 of 10

    A parallel, plan-based handover using a new Satellite Synchronized Function cuts LEO satellite handover latency to about 9 ms on average in an emulated prototype.

  5. DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection

    cs.CR 2025-05 reject novelty 5.0 of 10

    DP-TRAE combines a transferable white-box adversarial initialization with a memory-augmented superpixel query attack and reversible embedding to mislead black-box image classifiers while allowing image recovery.

  6. Rethinking Membership Inference Attacks Against Transfer Learning

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    A white-box attack on the student model can infer teacher-training membership in transfer learning by comparing the student's hidden representations with those of a shadow student model.

  7. Constructing 4D Radio Map in LEO Satellite Networks with Limited Samples

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    DeepRM uses neural networks to solve compressive sensing and tensor decomposition, reconstructing 4D radio maps (space plus frequency) with fewer samples and sensors than classical baselines.

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

    cs.LG 2025-01 conditional novelty 5.0 of 10

    A semi-supervised split learning framework with an auxiliary client model, adaptive pseudo-label thresholds, and activation interpolation improves training speed and accuracy over LEO satellite links.

  9. HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models

    cs.LG 2025-05 conditional novelty 4.0 of 10

    HSplitLoRA combines split learning with LoRA, selects important weights via a gradient-weight product metric, adapts LoRA ranks and model split points to device budgets, and aggregates heterogeneous adapters by concatenation.

  10. SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework

    cs.NI 2025-04 reject novelty 4.0 of 10

    An asynchronous split-federated learning framework for LEO satellite-ground networks enables local updates during disconnection and heterogeneous model splitting, with simulated accuracy gains over split learning and ...

  11. LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data

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    LCFed combines model splitting with clustered federated learning to share global and cluster-level knowledge, and uses low-rank model projections to cut clustering cost.

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