GitHub - shenzebang/Federated-Learning-Pytorchgithub.com/shenzebang/Federated-Learning-Pytorch 简述 从数据异质性和隐私的角度来看,类别不平衡问题在联邦学习(FL)的场景中显得更为突出。由于局部数据分布存在异质性,局部的不平衡情况与全局的不平衡情况之间可能会存在显著差异,也就是说,在局部属于少数类别的数据...
FEDn: An enterprise-ready and vendor-agnostic federated learning platform. - GitHub - scaleoutsystems/fedn: FEDn: An enterprise-ready and vendor-agnostic federated learning platform.
.github assets bindings/python configs docker k8s/coordinator rust scripts .dockerignore .gitignore CHANGELOG.md LICENSE README.md README.tpl ROADMAP.md README Apache-2.0 license xaynet Xaynet: Train on the Edge with Federated Learning Want a framework that supports federated learning on the edge...
表一MAML算法伪代码,出自论文Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 步骤一:随机初始化参数; 步骤二:开始循环; 步骤三:导入图片,随即对几个task进行采样,形成一个batch; 步骤四---七:声明一下,这里每次迭代涉及到两次参数更新,由于个人习惯,笔者将第一次称之为外循环,第二次称之为...
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This
联邦元学习Federated meta learning 是MAMLMAMLMAML全程模型无关元学习,Model-AgnosticMeta-Learning,可以用在监督学习、强化学习上,所以称为模型无关。但是我理解到的是,MAML用在分类问题上,那么模型...配图均来自该视频 引用论文MAML中的一句话介绍元学习: The goalofmeta-learningis to train amodelon a variety...
constructing hidden and cell states that capture sequential information. A skip connection incorporates the initial input into the LSTM output, thus preserving crucial temporal features and stabilizing the learning process. Layer normalization, when applied to the LSTM output, not only accelerates convergen...
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints - felisat/clustered-federated-learning
Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices. Most of the previous works about federated learning focus on the privacy protection and communication cost reduction. However, how to achieve fairness in federa...
FLT (Jamali-Rad2022FederatedData) - Sc. 2 Cent. 65.81 (±07.73) 88.29 (±02.92) 75.47 (±05.06) 97.71 (±01.73) 89.33 (±04.22) pFedSim (Tan2023PFedSim:Learning) - Sc. 2 Cent. 68.52 (±07.53) 88.33 (±02.72) 75.90 (±04.88) 97.62 (±03.02) 90.14 (±04.15) DFedAvgM (Sun2022De...