Data heterogeneity presents significant challenges for federated learning (FL). Recently, dataset distillation techniques have been introduced, and performed at the client level, to attempt to mitigate some of these challenges. In this paper, we propose a highly efficient FL dataset distillation ...
Dataset distillation has enabled vari- ous applications including continual learning [44, 45, 47], efficient neural architecture search [45, 47], federated learn- ing [11, 37, 50], and privacy-preserving ML [22, 37] for images, text, and medical imaging data. As ment...
Dataset distillation has emerged as a strategy to overcome the hurdles associated with large datasets by learning a compact set of synthetic data that retains essential information from the original dataset. While distilled data can be used to train high performing models, little is understood about ...
(e.g., continual learning, privacy, neural architecture search, etc.). This task was first introduced in the paperDataset Distillation[Tongzhou Wang et al., '18], along with a proposed algorithm using backpropagation through optimization steps. Then the task was first extended to the real-...
Common Representation Learning 通用表示学习 The convergence of the alignment methods and knowledge distillation 对齐方法与知识蒸馏的融合 3. 本文贡献 经典FL算法比如FedAvg和FedProx,其全局模型通过模型聚合(即参数平均)进行更新,然后将其广播回客户端并用作本地学习的初始模型。 然而,这种方法忽略了跨客户端的数据...
论文地址:FedMD: Heterogenous Federated Learning via Model Distillation 2019 NIPS 算法细节 本文设有一个共享数据集... Model Compression via Distillation and Quantization 论文笔记 https://www.cnblogs.com/dushuxiang/p/10304622.html 摘要 深度神经网络(DNN)继续取得重大进展,解决从图像分类到翻译或强化学习的...
c_learning cache_replacement caltrain cann capsule_em caql cascaded_networks cate cbertscore cell_embedder cell_mixer cfq cfq_pt_vs_sa charformer ciw_label_noise class_balanced_distillation clay cluster_gcn clustering_normalized_cuts cnn_quantization cochlear_implant code_as_...
**(1) What is Dataset Condensation, or Dataset Distillation?** Data Condensation, also known as Dataset Distillation, first introduced by Wang et al., aims to alleviate the training burden by synthesizing a small yet informative distilled dataset derived from the complete training dataset, w...
Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset. A dataset distillation algorithm takes as input a large real dataset to be distilled (training set), and outputs a small synthetic distilled ...
Multiple Instance Learning TCGA DTFD-MIL Cancer type classification TCGA SubOmiEmbed Classification TCGA MSI-H Transformer Papers Dataset Loaders Edit AddRemove No data loaders found. You cansubmit your data loader here. Tasks Edit CRC100K Musk v2 ...