Table 1 summarizes the models and datasets used in this work. ImageNet-1K Deng u. a. (2009): We use the subset of the ImageNet dataset containing DeepCAM Kurth u. a. (2018): DeepCAM dataset for image segmentation, which consists of Fractal-3K Kataoka u. a. (2022) A rendered dataset from the Visual Atom method Kataoka We also use the setting in Kataoka u. a. (2022) Table 2 shows the detail of our hyper-parameters. Specifically, We follow the guideline of'TorchVision' to train the ResNet-50 that uses the CosineLR To show the robustness of KAKURENBO, we also train ResNet-50 with different settings, e.g., ResNet-50 (A) setting, we follow the hyper-parameters reported in Goyal u. a. (2017). It is worth noting that KAKURENBO merely hides samples before the input pipeline. In this section, we present an analysis of the factors affecting KAKURENBO's performance, e.g., the The result shows that our method could dynamically hide the samples at each epoch.

This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of

Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build ajoint model byusing local data.

Communication overhead is a major bottleneck hampering the scalability of distributed machine learning systems. Recently, there has been a surge of interest in using gradient compression to improve the communication efficiency of distributed neural network training. Using 1-bit quantization, signSGD with majority vote achieves a 32x reduction in communication cost. However, its convergence is based on unrealistic assumptions and can diverge in practice. In this paper, we propose a general distributed compressed SGD with Nesterov's momentum.

Abstract--Knee Osteoarthritis (KOA) is a musculoskeletal condition that can cause significant limitations and impairments in daily activities, especially among older individuals. T o evaluate the severity of KOA, typically, X-ray images of the affected knee are analyzed, and a grade is assigned based on the Kellgren-Lawrence (KL) grading system, which classifies KOA severity into five levels, ranging from 0 to 4. This approach requires a high level of expertise and time and is susceptible to subjective interpretation, thereby introducing potential diagnostic inaccuracies. T o address this problem a stacked ensemble model of fine-tuned Convolutional Neural Networks (CNNs) was developed for two classification tasks: a binary classifier for detecting the presence of KOA, and a multiclass classifier for precise grading across the KL spectrum. The proposed stacked ensemble model consists of a diverse set of pre-trained architectures, including MobileNetV2, Y ou Only Look Once (YOLOv8), and DenseNet201 as base learners and Categorical Boosting (CatBoost) as the meta-learner . This proposed model had a balanced test accuracy of 73% in multiclass classification and 87.5% in binary classification, which is higher than previous works in extant literature. Knee Osteoarthritis (KOA) [1] is a degenerative musculoskeletal joint disease in which the knee cartilage breaks down over time.

In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are