Possible solution: Centralized learning Aggregate the data Train a model on the server Challenge: Laws or policies may forbid giving patients' data to others. 总之,无论是不同公司,还是公司内部出于各种利益、政策、等等都会存在着数据孤岛的情况。因此联邦学习应运而生。 二、Distributed learning vs. Fede...
Comparative Study of Federated Learning Vs Centralized LearningM., DivyaS., Jasmine K.Grenze International Journal of Engineering & Technology (GIJET)
Communication Architecture：分为centralized和distributed。在centralized的FL system中，one server is requir...
It’s worth remembering that federated learning is still at the stage where more research is being done than real applications. And as with all the benefits brought to the table, the approach has its drawbacks that are not faced in centralized learning. Here are the key ones. ...
Federated learning seems to address the challenges faced by the traditional centralized learning approaches. Dhada et al. [8] compared the performance of traditional collaborative prognosis with the FL, or more specifically, FedAvg, in place of exchanging the failure data. To accomplish this, the C...
Driven by privacy concerns and the visions of Deep Learning, the last four years have witnessed a paradigm shift in the applicability mechanism of Machine Learning (ML). An emerging model, called Federated Learning (FL), is rising above both centralized systems and on-site analysis, to be a ...
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond Authors Sawsan AbdulRahman, Hanine Tout, Hakim
Federated Learning Part 1: Introduction Federated Learning Comic Federated Learning: Collaborative Machine Learning without Centralized Training Data GDPR, Data Shotrage and AI (AAAI-19) Federated Learning: Machine Learning on Decentralized Data (Google I/O'19)...
Whereas adversarial training (AT) provides a sound solution for centralized learning, extending its usage for federated users has imposed significant challenges, as many users may have very limited training data and tight computational budgets, to afford the data-hungry and costly AT. In this paper...
2017年4月6日,谷歌科学家Brendan McMahan和Daniel Ramage在GoogleAI上发布名为《 Federated Learning: Collaborative Machine Learning without Centralized Training Data》的博文,介绍了Federated Learning也是一种机器学习,能够让用户通过移动设备交互来训练模型。 Google近期还特别推出中文漫画对于Federated Learning进行介绍,...