Heterogeneous Federated Learning (HFL) has received widespread attention due to its adaptability to different models and data. The HFL approach utilizing auxiliary models for knowledge transfer can further enhance flexibility. However, existing frameworks face the challenges of local overfitting and aggregation bias. To address these issues, we propose FedSCE. By restricting specific layers of the local model updates to a subspace, FedSCE reduces the degrees of freedom of the update, enhances generalization, and mitigates the risk of overfitting. The subspace is dynamically updated to ensure coverage of the latest model update trajectory. Additionally, FedSCE evaluates client contributions based on the update distance of the auxiliary model in feature space and parameter space, achieving adaptive weighted aggregation. We validate our approach in both feature-skewed and label-skewed scenarios, demonstrating that on Office10, our method exceeds the best baseline by 3.87%. The code will be available at https://github.com/AVC2-UESTC/FedSCE.git.

Recent advancements in deep neural networks have driven significant progress in image enhancement (IE). However, deploying deep learning models on resource-constrained platforms, such as mobile devices, remains challenging due to high computation and memory demands. To address these challenges and facilitate real-time IE on mobile, we introduce an extremely lightweight Convolutional Neural Network (CNN) framework with around 4K parameters. Our approach integrates reparameterization with an Incremental Weight Optimization strategy to ensure efficiency. Additionally, we enhance performance with a Feature Self-Transform module and a Hierarchical Dual-Path Attention mechanism, optimized with a Local Variance-Weighted loss. With this efficient framework, we are the first to achieve real-time IE inference at up to 1,100 frames per second (FPS) while delivering competitive image quality, achieving the best trade-off between speed and performance across multiple IE tasks. The code will be available at https://github.com/AVC2-UESTC/MobileIE.git.

Tensor decomposition is widely used in feature extraction, data analysis, and other fields. As a means of tensor decomposition, the robust tensor power method based on tensor sketch (TS-RTPM) can quickly mine the potential features of tensor, but in some cases, its approximation performance is limited. In this paper, we propose a data-driven framework called TS-RTPM-Net, which improves the estimation accuracy of TS-RTPM by jointly training the TS value matrices with the RTPM initial matrices. It also uses two greedy initialization algorithms to optimize the TS location matrices. In addition, TS-RTPM-Net accelerates TS-RTPM by using fast power iteration modules. Comparative experiments on real-world datasets verify that TS-RTPM-Net outperforms TS-RTPM in terms of estimation accuracy, running speed, and memory consumption.