现有联邦学习个性化技术《2020-IEEE-Survey of Personalization Techniques for Federated Learning》包括多任务学习《2017-NIPSFederated Multi-Task Learning》,基础层+个性化层《2019-Federated learning with personalization layers》,全局和局部模型的混合《2020-Federated learning of a mixture of global and local ...
论文笔记:ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks 天下客 机器学习、联邦学习、图神经网络14 人赞同了该文章 前言 GNN 是分析解决图机器学习问题的首选方法,但是由于用户方的隐私问题、法规限制和商业竞争等原因,集中大量的真实世界图数据用于GNN的训练是很困难的。
因此，联邦学习与这种联合学习是完全不同的概念。还有一种学习方法叫做“多任务学习”(multitask learning)[8,9]，它是迁移学习的一个子方向，旨在有多个学习目标并部分共用数据的情况下，尽量多地利用共有模型部分来提高学习效果。多任务学习对数据安全和隐私也没有提出要求，而是一种机器学习算法。联邦学习概述 什么...
Multi-task federated learning for personalized healthcare Multi-task federated learning (MTFL) is an emerging approach in personalized healthcare that combines the benefits of federated learning and multi-task learning. Federated learning enables the training of models on distributed datasets while ensuring...
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data. For example, one client might have patient data with "healthy'' pancreases only while datasets from other clients ...
Federated Multi-Task LearningVirginia SmithStanfordsmithv@stanford.eduChao-Kai Chiang ∗USCchaokaic@usc.eduMaziar Sanjabi ∗USCmaziarsanjabi@gmail.comAmeet TalwalkarCMUtalwalkar@cmu.eduAbstractFederated learning poses new statistical and systems challenges in training machinelearning models over distributed...
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model training on multi-task learning datasets. However, a comprehensive evaluation method, integrating the unique features of both FL...
Similarly, Yu et al. (2020) build a federated multi- task learning framework for smart home IOT to automatically learn users’ behavior patterns, which could effectively detect physical hazards. Furthermore, Liu, Wang, Liu, and Xu (2020) proposed a data fusion approach based on FL for robots...
2.2.1 多任务学习 Multitask learning (MTL) 多任务学习的目标是训练一个联合执行几个相关任务的模型。这可以通过利用特定领域的知识来提高学习任务的泛化性。在MTL中,通过将每个FL客户视为一个任务,有可能学习和捕捉客户之间由其异质的本地...
联邦多任务学习(Federated MultiTask Learning):考虑到本地数据隐私,联邦学习在大量移动设备上协作训练 模型。此设置还可以扩展到联合多任务学习环境,其中多任务学习驱动设备之间个性化但共享的模型。 可信执行环境(Trusted Execution Environments):这个在笔记04中详细提及过,这里不展开了。 数据清理(Data Sanitization):同...