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Zhao, Z. (author), Huang, J. (author), Chen, Lydia Y. (author), Roos, S. (author)
Generative Adversarial Networks (GANs) are increasingly adopted by the industry to synthesize realistic images using competing generator and discriminator neural networks. Due to data not being centrally available, Multi-Discriminator (MD)-GANs training frameworks employ multiple discriminators that have direct access to the real data....
conference paper 2024
document
Xu, J. (author), Hong, C. (author), Huang, J. (author), Chen, Lydia Y. (author), Decouchant, Jérémie (author)
Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to compute a global model. Depending on the aggregation method used, the local updates are either the gradients or the weights of local learning models, e.g., FedAvg...
conference paper 2023
document
Huang, J. (author), Zhao, Z. (author), Chen, Lydia Y. (author), Roos, S. (author)
Attacks on Federated Learning (FL) can severely reduce the quality of the generated models and limit the usefulness of this emerging learning paradigm that enables on-premise decentralized learning. However, existing untargeted attacks are not practical for many scenarios as they assume that i) the attacker knows every update of benign...
conference paper 2023
document
Huang, J. (author), Hong, C. (author), Liu, Yang (author), Chen, Lydia Y. (author), Roos, S. (author)
Federated learning (FL) enables collaborative learning between parties, called clients, without sharing the original and potentially sensitive data. To ensure fast convergence in the presence of such heterogeneous clients, it is imperative to timely select clients who can effectively contribute to learning. A realistic but overlooked case of...
conference paper 2023
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Chen, Peiran (author), Calis, Metin (author), Wijkstra, Hessel (author), Huang, Pintong (author), Hunyadi, Borbala (author), Mischi, Massimo (author)
A cost-effective, widely available, and practical diagnostic imaging tool for prostate cancer (PCa) localization is still lacking. Recently, the contrast-ultrasound dispersion imaging (CUDI) technique has been developed for PCa localization by quantifying dynamic contrast-enhanced ultrasound (DCE-US) acquisitions. Tissue stiffness is an...
conference paper 2022
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Huang, J. (author), Talbi, Rania (author), Zhao, Z. (author), Boucchenak, Sara (author), Chen, Lydia Y. (author), Roos, S. (author)
Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models...
conference paper 2020
document
Li, K. (author), Huang, R. (author), Phang, S.K. (author), Lai, S. (author), Wang, F. (author), Tan, P. (author), Chen, B.M. (author), Lee, T.H. (author)
In this paper, we propose an approach to autonomously control a quadrotor micro aerial vehicle (MAV). With take-off weight of 50 g and 8-min flight endurance, the MAV platform codenamed ‘KayLion’ developed by the National University of Singapore (NUS) is able to perform autonomous flight with pre-planned path tracking. The vision-based...
conference paper 2014
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