Robust tensor decomposition (RTD) is designed to distinguish low-rank and sparse tensors from noisy high-dimensional data, which holds fundamental significance in the fields of machine learning and computer vision. Nevertheless, current RTD-based methods fall short in addressing the issues of automatic noise adaptation and determination of model capacity. In response to these challenges, this paper introduces a robust non-parametric Bayesian method known as the Bayesian adaptive tensor ring decomposition (BATR) method. More precisely, BATR models unknown noise using a Dirichlet process Gaussian mixture model (DP-GMM), with automatic determination of the noise components. Besides, BATR accomplishes an adaptive low tensor ring (TR) rank model by incorporating a more advanced generalized hyperbolic (GH) prior into the probabilistic framework, thereby facilitating automatic TR rank determination. Furthermore, a variational Bayesian inference algorithm is employed to update the posteriors of the model. Extensive experiments on synthetic data, color images, face images, multispectral images, and hyperspectral images demonstrate the improved performance of BATR compared to other state-of-the-art methods.
The advent of Large Language Models (LLMs) represents a leap toward leveraging AI for the benefit of humanity. However, the realization of this potential requires addressing the growing need for personalization and data privacy across diverse entities. Federated Learning (FL) offers a vital paradigm for developing private and personalized LLMs, yet its effectiveness is often limited by the inequality of computational resources at the edge. Existing federated Low-Rank Adaptation (LoRA) struggles to accommodate such heterogeneous hardware capacities, leading to information loss during model aggregation. To bridge this gap, this paper presents an adaptive aggregation framework designed to optimize federated LLM fine-tuning under these constrained conditions. The framework introduces a dynamic budgeting mechanism that quantifies personalization intensity through Frobenius norm divergence and allocates rank capacity accordingly. To resolve rank heterogeneity, a dimension-aligned strategy based on Singular Value Decomposition (SVD) is applied, enabling the consistent fusion of updates across diverse devices. Extensive experiments on the General Language Understanding Evaluation (GLUE) benchmark using Llama-3-8B achieve an average score of 0.6551 and a mean task ranking of 2.22. The results indicate that our method improves global generalization while preserving client-specific personalization in resource-limited environments.
Jinhua Chen, Yuning Qiu, Franck Junior Aboya Messou et al.· 2026 ITU Kaleidoscope - AI a...· 0 citations