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A federated learning framework for deep imputation of missing data in heterogeneous ICU time series. [PDF]
Sci RepVavekanand R, Sathio AA, Sultani M.
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A federated learning with Large-Small Kernel Attention Network for image classification. [PDF]
Front Plant SciLiu T, Xie J, Dong H.
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Optimization of cross-institutional medical federated learning framework driven by confidential computing. [PDF]
Sci RepXu F, Wei X, Zhao Z, Sun P.
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Federated learning for fair autism spectrum disorder screening across age-heterogeneous populations. [PDF]
Front Digit HealthRekik S, Mehmood S, Berriche L.
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IEEE Consumer Electronics Magazine, 2021
Now we are in an era of technology transformation in our everyday life, where data play a key role in the decision making and bringing the action into reality. These data are collected from many distributed sources. Another important concept in this process is machine learning (ML) and data analytics.
Niranjan Kumar Ray 0001 +2 more
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Now we are in an era of technology transformation in our everyday life, where data play a key role in the decision making and bringing the action into reality. These data are collected from many distributed sources. Another important concept in this process is machine learning (ML) and data analytics.
Niranjan Kumar Ray 0001 +2 more
openaire +1 more source
A survey on federated learning
Knowledge-Based Systems, 2021Abstract Federated learning is a set-up in which multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator. This setting also allows the training data decentralized to ensure the data privacy of each device.
Chen Zhang, Yu Xie, Hang Bai
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On Decentralizing Federated Learning
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020Federated Learning (FL), a distributed version of Deep Learning (DL), was introduced to tackle the problem of user privacy and huge bandwidth requirements in sending the user data to the company servers that run DL models. FL enables on-device training of the models.
Akul Agrawal +2 more
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Federated learning and privacy
Communications of the ACM, 2022Building privacy-preserving systems for machine learning and data science on decentralized data.
Kallista A. Bonawitz +3 more
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