Clement Fung
Geciteerd door
Geciteerd door
Mitigating Sybils in Federated Learning Poisoning
C Fung, CJM Yoon, I Beschastnikh
arXiv preprint arXiv:1808.04866, 2018
Biscotti: A Blockchain System for Private and Secure Federated Learning
M Shayan, C Fung, CJM Yoon, I Beschastnikh
IEEE Transactions on Parallel and Distributed Systems 32 (7), 1513-1525, 2020
The Limitations of Federated Learning in Sybil Settings
C Fung, CJM Yoon, I Beschastnikh
23rd International Symposium on Research in Attacks, Intrusions and Defenses …, 2020
BPG: Seamless, automated and interactive visualization of scientific data
C P’ng, J Green, LC Chong, D Waggott, SD Prokopec, M Shamsi, ...
BMC bioinformatics 20 (1), 1-5, 2019
A bedr way of genomic interval processing
S Haider, D Waggott, E Lalonde, C Fung, FF Liu, PC Boutros
Source code for biology and medicine 11, 1-7, 2016
GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery
D Dao, C Cang, C Fung, M Zhang, N Pawlowski, R Gonzales, N Beglinger, ...
ICML 2019 Workshop: Climate Change - How can AI help?, 2019
Towards a Lightweight, Hybrid Approach for Detecting DOM XSS Vulnerabilities with Machine Learning
W Melicher, C Fung, L Bauer, L Jia
Proceedings of the Web Conference 2021, 2684-2695, 2021
Dancing in the dark: Private multi-party machine learning in an untrusted setting
C Fung, J Koerner, S Grant, I Beschastnikh
arXiv preprint arXiv:1811.09712, 2018
Brokered Agreements in Multi-Party Machine Learning
C Fung, I Beschastnikh
Proceedings of the 10th ACM SIGOPS Asia-Pacific Workshop on Systems, 69-75, 2019
Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems
C Fung, S Srinarasi, K Lucas, HB Phee, L Bauer
27th European Symposium on Research in Computer Security, 493-513, 2022
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Artikelen 1–10