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Private, Fair and Secure Collaborative Learning Framework f..:

, In: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing,

Roy, Debaditya ; Lekssays, Ahmed ; Girdzijauskas, Sarunas.. - p. 352-358 , 2023

Link: https://dl.acm.org/doi/10.1145/3594739.3610675

RT 
T1 <cTitle>Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing</cTitle>: T1 <Title>Private, Fair and Secure Collaborative Learning Framework for Human Activity Recognition</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3610675&Exemplar=1&LAN=DE
				
A1 Roy, Debaditya
A1 Lekssays, Ahmed
A1 Girdzijauskas, Sarunas
A1 Carminati, Barbara
A1 Ferrari, Elena
PB ACM
YR 2023
K1 differential privacy
K1 federated learning neural networks
K1 human activity recognition
K1 security
K1 Human-centered computing
K1 Ubiquitous and mobile computing
K1 Ubiquitous and mobile computing theory, concepts and paradigms
K1 Ubiquitous computing
K1 Security and privacy
K1 Human and societal aspects of security and privacy
K1 Privacy protections
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Supervised learning
K1 Supervised learning by classification
SP 352
OP 358
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3594739.3610675
DO https://dl.acm.org/doi/10.1145/3594739.3610675
SF ELIB - SuUB Bremen

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