… on convergence performance and privacy levels. … NbAFL and analyze the privacy performance
based on DP. In … the relationship between privacy levels, convergence performance, the …

… , we consider differential privacy, one of the most popular and powerful definitions of privacy.
We explore the interplay between machine learning and differential privacy, namely privacy-…

… Abstract—The research paper [1] proposes a differential privacy algorithm in the context
of Federated Learning and provides its performance analysis, mainly focusing on proving a …

… statistics and analysis of such data will seriously threaten the privacy of participating users.
Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model …

… Local Differential Privacy (LDP) can provide each user with strong privacy guarantees …
Due to its powerfulness, LDP has been widely adopted to protect privacy in various tasks (eg, …

… Different from centralized differential privacy, local differential privacy (LDP) allows users to
… the privacy of the data while also relieving it from the burden of preserving the data privacy. …

… -differential privacy on analyses that involve solving an optimization problem. The main idea
is to enforce ϵ-differential privacy … then naturally satisfies ϵ-differential privacy as well. Note …

… non-IID data on the performance of FL, specifically FedAvg. Furthermore, we provide a
privacy analysis of the method through the differential privacy framework, suggesting that FL can …

… differential privacy preserving in regression analysis. It provides improvement to allocate
privacy … At the same time, it shows good performance in privacy and utility of regression models. …

… The strong privacy guarantee of differential privacy comes at … results, while still satisfying
differential privacy. Different types … new query or analysis chips away at the total privacy budget …