Human Activity Recognition Using Federated Learning

State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mi...

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Vydané v:	2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) s. 1103 - 1111
Hlavní autori:	Sozinov, Konstantin, Vlassov, Vladimir, Girdzijauskas, Sarunas
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.12.2018
Predmet:	Accelerometers Activity recognition Computational modeling Data models Distributed databases Distributed Machine Learning Federated Learning Human Activity Recognition Privacy Task analysis Training
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