HeRD: Modeling Heterogeneous Degradations for Federated Super-Resolution in Satellite Imagery
Federated learning (FL) offers a privacy-preserving solution for single-image super-resolution (SR) on sensitive satellite imagery, but its performance is often hindered by simplistic data models. Existing methods that rely on simple bicubic downsampling fail to capture the complex, client-specific...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11083581/ |
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| Summary: | Federated learning (FL) offers a privacy-preserving solution for single-image super-resolution (SR) on sensitive satellite imagery, but its performance is often hindered by simplistic data models. Existing methods that rely on simple bicubic downsampling fail to capture the complex, client-specific degradations found in real-world satellite data, creating a significant domain gap. To address this, we propose a novel strategy, Heterogeneous Realistic Degradation (HeRD), which models data heterogeneity by generating realistic low-resolution images based on the unique, device-locked characteristics of different satellites. Unlike conventional approaches, HeRD systematically applies diverse, anisotropic degradations to enable fine-grained control over non-Independent and Identically Distributed (non-IID) conditions. Our extensive evaluations demonstrate the robustness of FL when trained with HeRD. The proposed federated pipeline outperforms traditional bicubic-based setups by over 0.5 dB in PSNR. Notably, even in highly heterogeneous environments, our approach achieves performance within just 0.2–0.4 dB of a fully centralized training model. These findings confirm that HeRD provides a viable, high-performance, and privacy-preserving alternative for super-resolving distributed satellite imagery where data sovereignty and disparate hardware characteristics are paramount.<xref ref-type="fn" rid="fn1">1</xref> |
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| ISSN: | 2169-3536 |