2 papers
cs.LG2026
Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi +1
Federated learning (FL) enables multiple data holders to train machine learning models collaboratively without centralizing raw data, making it useful in privacy sensitive domains…
cs.CR2026
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias à kesson, Johan Ãstman +3
Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable exp…