IMDEA Networks
Dr. Tianyue CHU
Ingeniera de investigación en Telefónica Research. Madrid. España
- Fecha de graduación: Jul 2025
- Afiliación: IMDEA Networks Institute, Universidad Carlos III de Madrid
- BSc: Double Bachelor's Degree. Mathematics and Applied Mathematics & Finance - Shenzhen University. Shenzhen, China
- MSc: Statistics - Shenzhen University. Shenzhen, China
- PhD: Ingeniería Telemática - Universidad Carlos III de Madrid. Madrid, España
- Tesis doctoral: Security-Enhanced and Privacy-Preserving Federated Learning
- Supervisor de tesis: Dr. Nikolaos LAOUTARIS
- Puesto anterior: Research Assistant. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. Shenzhen. China
- Fecha de incorporación: Octubre 2020
- Fecha de cese: Marzo 2025
- Email: tianyue.chuimdea.org
Publicaciones
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning
Tianyue Chu, Mengwei Yang, Nikolaos Laoutaris, Athina Markopoulou
ACM Transactions on Modeling and Performance Evaluation of Computing Systems. 10.1145/3702241. Noviembre 2024
Information-Theoretical Bounds on Privacy Leakage in Pruned Federated Learning
Tianyue Chu, Mengwei Yang, Nikolaos Laoutaris, Athina Markopoulou
ISIT 2024 Workshop on Information-Theoretic Methods for Trustworthy Machine Learning. Athens, Greece. Julio 2024
FedQV: Leveraging Quadratic Voting in Federated Learning
Tianyue Chu, Nikolaos Laoutaris
ACM SIGMETRICS. Venice, Italy. Junio 2024
Strengthening Privacy in Robust Federated Learning through Secure Aggregation
Tianyue Chu, Devriş Işler, Nikolaos Laoutaris
Workshop on Artificial Intelligence System with Confidential Computing (AISCC 2024), co-located with NDSS Symposium 2024. San Diego, CA, USA. Febrero 2024
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Pre-impact alarm system for fall detection using MEMS sensors and HMM-based SVM classifiercall_made
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Accidental fall can cause physical injury, fracture and other health complications, especially for elderly people living alone. Aimed to provide timely assistance after the occurrence of falling down, a pre-fall alarm system was proposed. In order to test the reliability of the pre-fall alarm system, eighteen subjects who wore this device on the waist were required to participate in a series of experiments. The acceleration and angular velocity time series extracted from human motion processes were used to describe human motion features. HMM-based SVM classifier was used to determine the maximum separation boundary between falls and Activities of Daily Living (ADLs). The fall detection results showed 94.91% accuracy, 97.22% Sensitivity and 93.75% Specificity. The proposed device can accurately recognize fall events, achieve additional functions, and have the advantages of small size and low power consumption. Based on the findings, this pre-impact fall alarm system with a detection algorithm could potentially be useful for monitoring the state of physical function in elderly population.