Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of intermediate tensors, which exacerbate errors under frequent communication and introduce significant computational overhead during compression. To this end, we propose TACO (Tensor-parallel Adaptive COmmunication compression), a robust FP8-based framework for compressing TP intermediate tensors. First, we employ a data-driven reshaping strategy combined with an Adaptive Scale–Hadamard Transform to enable high-fidelity FP8 quantization, while its Dual-Scale Quantization mechanism ensures numerical stability throughout training. Second, we design a highly fused compression operator to reduce memory traffic and kernel launch overhead, allowing efficient overlap with communication. Finally, we integrate TACO with existing state-of-the-art methods for Data and Pipeline Parallelism to develop a compression-enabled 3D-parallel training framework. Detailed experiments on GPT models and Qwen model demonstrate up to 1.87 × end-to-end throughput improvement while maintaining near-lossless accuracy, validating the effectiveness and efficiency of TACO in large-scale training.

Collective communication is critical to scaling large language model (LLM) training across various parallelism strategies, including data, tensor, and pipeline parallelism on GPU clusters. However, as model sizes and training scales increase, communication overhead is emerging as a major performance bottleneck. While compression is a promising mitigation strategy, existing solutions often lack user-transparency, hinder deployment and extensibility, and are not co-designed with communication algorithms. To address these limitations, we present COCCL, a high-performance collective communication library built on top of NCCL. COCCL introduces a novel programming model that can easily integrate compression into communication workflows with flexible configurability. It features a suite of compression-aware collective algorithms and runtime overlap mechanisms that mitigate error propagation and reduce computational overhead. We integrate well-established compression techniques into COCCL and tune the compression configurations during 3D-parallel training on GPT and Qwen models with up to 7 billion parameters. Using the optimal configuration (COCCL-3D), we achieve 1.24× throughput improvement while maintaining training accuracy.

Transfer learning with pretrained models from general computer vision tasks is widely adopted in remote sensing, offering reduced training costs and improved performance across applications like scene classification and semantic segmentation. However, this practice exposes downstream tasks to significant vulnerabilities, as adversaries can exploit publicly available pre-trained models to craft attacks that compromise model integrity. This paper introduces Adversarial Neuron Manipulation (ANM), a novel attack strategy with two variants—ANM-S (Single Neuron) and ANM-M (Multiple Neurons)—that generates highly transferable perturbations by selectively targeting fragile neurons in pretrained models. Unlike existing adversarial attacks, ANM requires no domain-specific knowledge, enhancing its applicability and efficiency across diverse settings. Evaluated on various benchmark datasets, ANM significantly degrades model performance. ANM-M, in particular, reduces accuracy by up to 84.24% in scene classification, 42.47% in semantic segmentation, and 75.33% in object detection (mAP). These results reveal critical weaknesses in deep learning models, especially their susceptibility to transferable attacks across CNN and Transformer architectures. By exposing the security risks inherent in transfer learning for remote sensing, ANM underscores the urgent need for robust defense mechanisms to safeguard safety-critical applications. Our findings advocate for enhanced model resilience and motivate future research into countering sophisticated adversarial threats.

The term frequency-inverse document frequency (TF-IDF) is a term weighting method that is widely used for textual content analysis. However, when using TF-IDF for classification on unbalanced, distributed Chinese Internet media content, it is unreasonable for IDF to equate the inverse text high frequency having strong distinguishing ability. In this paper, the computational formula for calculating collection frequency is optimized by combining it with the average value of document frequency (D̅F̅). This combination helps to reduce the influence caused by any unbalanced distribution of content on the Internet. A novel computational formula based on (D̅F̅) is designed as a collection frequency factor, combining with term frequency (TF) to form a new term weighting method named TF-ISDF. Experiments on support vector machine (SVM) and Random Forests (RF) algorithms are demonstrated to validate the two different term weighting schemes (TWS). The results show that the proposed TF-ISDF approach exhibits better performance than TF-IDF in both classification algorithms and analysis of Chinese Internet media content.