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cs.CR2022
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning
Hua Ma, Qun Li, Yifeng Zheng +5
Federated Learning (FL), a distributed machine learning paradigm, has been adapted to mitigate privacy concerns for customers. Despite their appeal, there are various inference att…
cs.CR2022
Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
Xicheng Wan, Yifeng Zheng, Qun Li +3
Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datase…
cs.CR2021
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning
Yansong Gao, Qun Li, Yifeng Zheng +3
Training high-performing deep learning models require a rich amount of data which is usually distributed among multiple data sources in practice. Simply centralizing these multi-so…