Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction
This work addresses multimodal inverse problems in federated learning, offering an incremental improvement in communication efficiency and reconstruction quality for distributed image reconstruction tasks.
The paper tackled multimodal image reconstruction in federated learning by developing a federated low-rank tensor estimation method using Tucker decomposition with joint factorization and randomized sketching, achieving superior reconstruction quality and communication compression compared to existing approaches.
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.