10 citations · 12 across the 5 of their papers we have counts for
4 papers · 1 filter
A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines
Bowen Li, Xiufeng Liu, Maria Sinziiana Astefanoaei
Accurate short-term wind power forecasting is essential for grid dispatch and market operations, yet centralising turbine data raises privacy, cost, and heterogeneity concerns. We…
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning
Jian Zhang, Bowen Li Jie Li, Chentao Wu
In response to legislation mandating companies to honor the \textit{right to be forgotten} by erasing user data, it has become imperative to enable data removal in Vertical Federat…
Temporal Gradient Inversion Attacks with Robust Optimization
Bowen Li, Hanlin Gu, Ruoxin Chen +5
Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged…
Federated Deep Learning with Bayesian Privacy
Hanlin Gu, Lixin Fan, Bowen Li +3
Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with…