MACHINE learningDATA augmentationImage classification is crucial for various applications, including digital construction, smart manufacturing, and medical imaging. Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification, in this paper, we propose a federated...
【ICLR 2022】On Bridging Generic and Personalized Federated Learning for Image Classification FL的标准设置寻求训练能够很好地处理通用数据分布的单一的“全局”模型(generic FL) FL的另一种设置试图通过为每个客户构建与客户的个性化数据捆绑在一起的“个性化”模型来承认客户之间的异质性(personalized FL)(为每个客户...
图像分类 Image Classification 多模态数据集 Multimodal Federated Learning is a collaborative training process involving multiple clients, each with diverse modality settings and data, conducting learning tasks without disclosing their local raw data. 多模态联邦学习(Multimodal Federated Learning, MMFL)是一种涉...
Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets cu...
The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly availabl
Data heterogeneity across medical centers, resulting in a coupling of universal information for classification tasks and personalized information for private dataset within local models, is still a difficult challenge in personalized federated learning (PFL). Moreover, the high interclass similarity in the...
Deep learningDronesFederated learningMilitary areaAccurate classification of military-focused areas using machine learning techniques is crucial for meeting military criteria. However, preserving high data privacy in aerial image classification poses significant challenges. This paper proposes a Privacy-Preserving...
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19 showed that for an image classification task, the performance of their data-private collaborative models dropped by up to 55% depending on how much institutional bias (degree of non-IID) they introduce when sharding (i.e., partitioning) a single dataset into hypothetical institutions. The ...
On Bridging Generic and Personalized Federated Learning for Image Classification The Ohio State University code Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond KAIST;MIT ICLR 2021 Federated Learning Based on Dynamic Regularization Boston University;ARM Achieving Linear Speedup ...