Federated Learning -- Methods, Applications and beyond

Abstract

In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress.While some domains like web analysis can benefit from this with only minor restrictions, other fields like in medicine with patient data are strongerregulated. In particular \emph{data privacy} plays an important role as recently highlighted by the trustworthy AI initiative of the EU or general privacy regulations in legislation. Another major challenge is, that the required training \emph{data is} often \emph{distributed} in terms of features or samples and unavailable for classicalbatch learning approaches. In 2016 Google came up with a framework, called \emph{Federated Learning} to solve both of these problems. We provide a brief overview on existing Methods and Applications in the field of vertical and horizontal \emph{Federated Learning}, as well as \emph{Fderated Transfer Learning}.

References in corpus (20)

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• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Secure Federated Transfer Learning
• Federated Machine Learning: Concept and Applications
• Applied Federated Learning: Improving Google Keyboard Query Suggestions
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• Federated Adversarial Domain Adaptation
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• Bayesian Nonparametric Federated Learning of Neural Networks
• Abnormal Client Behavior Detection in Federated Learning
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• Privacy-preserving Federated Brain Tumour Segmentation
• Local Differential Privacy based Federated Learning for Internet of Things
• Federated Learning for Ranking Browser History Suggestions
• FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
• Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
• Accelerating Federated Learning via Momentum Gradient Descent