… Federated Learning (FL) is emerging as a promising technology to build machine learning
models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on …

… • Finally, we demonstrate that federated learning can perform localization accuracy (up to
below 1 meters) close to centralized training without violating user privacy, and it can boost …

… accountability methods for federated learning systems, our … trusted auditors; it is fully
decentralized and resilient up to a 50% … of BlockFLow as an accountable federated learning …

… Abstract—Federated learning (FL) enables collaborative train- ing of machine learning (ML)
models while preserving user data privacy. Existing FL approaches can potentially facilitate …

… to explore user-level privacy leakage against the federated learning by the attack from a …
the privacy risk of federated learning, which is considered as a privacy-preserving learning …

… 3) We integrate differential privacy into federated learning to further protect data privacy. We
… In Section III, we describe permissioned blockchain and federated learning for data sharing …

… a novel privacy preserving FL framework based on the concept of differential privacy (DP). To
… satisfy the DP guarantee with adjustable privacy protection levels by varying the variances …

… This may raise privacy concerns since the privacy-sensitive data are owned by different …
This paper proposes a federated learning (FL) based deep learning model to identify household …

… practical privacy-preserving federated learning schemes are … membership proof for federated
learning, which leverages … we propose a privacy-preserving federated learning scheme …

… security of our privacy-preserving aggregation scheme and compare it with the existing
schemes. Finally, we evaluate the performance of our federated learning model and compare it …