Journal ArticleDOI
A review of applications in federated learning
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This study reviews FL and explores the main evolution path for issues exist in FL development process to advance the understanding of FL, and identifies six research fronts to address FL literature and help advance theUnderstanding of FL for future optimization.About:
This article is published in Computers & Industrial Engineering.The article was published on 2020-11-01. It has received 316 citations till now.read more
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Journal ArticleDOI
Federated Learning for Healthcare: Systematic Review and Architecture Proposal
Rodolfo Stoffel Antunes,Cristiano André da Costa,Arne Küderle,Imrana Abdullahi Yari,Bjoern M. Eskofier +4 more
TL;DR: A systematic literature review on current research about federated learning in the context of EHR data for healthcare applications and discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review.
Journal ArticleDOI
Electricity Consumer Characteristics Identification: A Federated Learning Approach
TL;DR: A distributed electricity consumer characteristics identification method is proposed based on federated learning, which can preserve the privacy of retailers and has comparable performance with the centralized model on both balanced and unbalanced datasets.
Journal ArticleDOI
Federated Learning in Edge Computing: A Systematic Survey
TL;DR: A systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems is provided to help researchers better understand the connection between FL and EC enabling technologies and concepts.
Journal ArticleDOI
Federated learning review: Fundamentals, enabling technologies, and future applications
TL;DR: Federated Learning (FL) has been foundational in improving the performance of a wide range of applications since it was first introduced by Google and some of the most prominent and commonly used FL-powered applications are Android's Gboard for predictive text and Google Assistant as mentioned in this paper .
Journal ArticleDOI
Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future directions
TL;DR: In this article , the authors present an extensive survey on the various smart city based applications of FL models in DTs and present some prominent challenges and future directions for better FL-DT integration in future applications.
References
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Posted Content
Communication-Efficient Learning of Deep Networks from Decentralized Data
TL;DR: This work presents a practical method for the federated learning of deep networks based on iterative model averaging, and conducts an extensive empirical evaluation, considering five different model architectures and four datasets.
Proceedings Article
Similarity Search in High Dimensions via Hashing
TL;DR: Experimental results indicate that the novel scheme for approximate similarity search based on hashing scales well even for a relatively large number of dimensions, and provides experimental evidence that the method gives improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition.
Proceedings Article
Communication-Efficient Learning of Deep Networks from Decentralized Data
TL;DR: In this paper, the authors presented a decentralized approach for federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets.
Posted Content
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný,H. Brendan McMahan,Felix X. Yu,Peter Richtárik,Ananda Theertha Suresh,Dave Bacon +5 more
TL;DR: Two ways to reduce the uplink communication costs are proposed: structured updates, where the user directly learns an update from a restricted space parametrized using a smaller number of variables, e.g. either low-rank or a random mask; and sketched updates, which learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling.
Journal ArticleDOI
Federated Machine Learning: Concept and Applications
TL;DR: This work introduces a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federatedLearning, and federated transfer learning, and provides a comprehensive survey of existing works on this subject.