mlsanityData to 14 Oct 2026

Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction

arXiv 2502.02761 ↗First posted 4 Feb 2025cs.LG cs.CV cs.DCcited by 0

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Abstract (arXiv)

Low-rank tensor estimation offers a powerful approach to addressing high-dimensional data challenges and can substantially improve solutions to ill-posed inverse problems, such as image reconstruction under noisy or undersampled conditions. Meanwhile, tensor decomposition has gained prominence in federated learning (FL) due to its effectiveness in exploiting latent space structure and its capacity to enhance communication efficiency. In this paper, we present a federated image reconstruction method that applies Tucker decomposition, incorporating joint factorization and randomized sketching to manage large-scale, multimodal data. Our approach avoids reconstructing full-size tensors and supports heterogeneous ranks, allowing clients to select personalized decomposition ranks based on prior knowledge or communication capacity. Numerical results demonstrate that our method achieves superior reconstruction quality and communication compression compared to existing approaches, thereby highlighting its potential for multimodal inverse problems in the FL setting.

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References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  3. 2024FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuningcited by 59
  4. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  5. 2020An Optimal Statistical and Computational Framework for Generalized Tensor Estimationcited by 39
  6. 2021FEDZIP: A Compression Framework for Communication-Efficient Federated Learningcited by 27
  7. 2013Tucker Tensor Regression and Neuroimaging Analysiscited by 26
  8. 2013Tensor-based formulation and nuclear norm regularization for multi-energy computed tomographycited by 22
  9. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  10. 2019Tensor Regression Using Low-rank and Sparse Tucker Decompositionscited by 10
  11. 2014Decomposition of Big Tensors With Low Multilinear Rankcited by 7
  12. 2024Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularizationcited by 6
3 more
  1. 2024Federated Learning Using Coupled Tensor Train Decompositioncited by 3
  2. 2024FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learningcited by 1
  3. 2025FIRM: Federated Image Reconstruction using Multimodal Tomographic Datacited by 1

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