FIRM: Federated Image Reconstruction using Multimodal Tomographic Data
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912182356148224 |
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| author | Byeon, Geunyeong Ryu, Minseok Di, Zichao Wendy Kim, Kibaek |
| author_facet | Byeon, Geunyeong Ryu, Minseok Di, Zichao Wendy Kim, Kibaek |
| contents | We propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal approaches that are prone to noise and reduced image quality. Our approach formulates a joint inverse optimization problem incorporating multimodality constraints and solves it in a federated framework through local gradient computations complemented by lightweight central operations, ensuring data decentralization. Leveraging the connection between our federated algorithm and the quadratic penalty method, we introduce an adaptive step-size rule with guaranteed sublinear convergence and further suggest its extension to augmented Lagrangian framework. Numerical results demonstrate its superior computational efficiency and improved image reconstruction quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FIRM: Federated Image Reconstruction using Multimodal Tomographic Data Byeon, Geunyeong Ryu, Minseok Di, Zichao Wendy Kim, Kibaek Optimization and Control We propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal approaches that are prone to noise and reduced image quality. Our approach formulates a joint inverse optimization problem incorporating multimodality constraints and solves it in a federated framework through local gradient computations complemented by lightweight central operations, ensuring data decentralization. Leveraging the connection between our federated algorithm and the quadratic penalty method, we introduce an adaptive step-size rule with guaranteed sublinear convergence and further suggest its extension to augmented Lagrangian framework. Numerical results demonstrate its superior computational efficiency and improved image reconstruction quality. |
| title | FIRM: Federated Image Reconstruction using Multimodal Tomographic Data |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2501.05642 |