2 papers
quant-ph2025
Experimentally validated quantum-secure federated learning over a multi-user quantum network
Zhi-Ping Liu, Xiao-Yu Cao, Hao-Wen Liu +6
Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum federated learning (QFL) offers a promis…
cs.LG2024
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li, Lele Fu, Tong Wang +6
To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic…