Tackling noisy clients in federated learning with end-to-end label correction
Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive
applications without sacrificing the sensitive private information of clients. However, the data …
applications without sacrificing the sensitive private information of clients. However, the data …
Multi-domains personalized local differential privacy frequency estimation mechanism for utility optimization
Abstract Local Differential Privacy (LDP) has garnered considerable attention in recent years
because it does not rely on trusted third parties and has low interactivity and high …
because it does not rely on trusted third parties and has low interactivity and high …
Enhanced Privacy Bound for Shuffle Model with Personalized Privacy
The shuffle model of Differential Privacy (DP) is an enhanced privacy protocol which
significantly amplifies the central DP guarantee by anonymizing and shuffling the local …
significantly amplifies the central DP guarantee by anonymizing and shuffling the local …