3 citations · 3 across the 4 of their papers we have counts for
4 papers
Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures
Jiahui Wu, Fucai Luo, Tiecheng Sun +2
The privacy-preserving federated learning schemes based on the setting of two honest-but-curious and non-colluding servers offer promising solutions in terms of security and effici…
Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning
Jiahui Wu, Tiecheng Sun, Fucai Luo +2
Multi-Key Homomorphic Encryption (MKHE), proposed by Lopez-Alt et al. (STOC 2012), allows for performing arithmetic computations directly on ciphertexts encrypted under distinct ke…
FSSA: Efficient 3-Round Secure Aggregation for Privacy-Preserving Federated Learning
Fucai Luo, Saif Al-Kuwari, Haiyan Wang +1
Federated learning (FL) allows a large number of clients to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggrega…
ESAFL: Efficient Secure Additively Homomorphic Encryption for Cross-Silo Federated Learning
Jiahui Wu, Weizhe Zhang, Fucai Luo
Cross-silo federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing training data, but privacy in FL remains a major chall…