Showing cs.LGShow all
2 papers · 1 filter
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.LG2024
Poisoning Attacks on Federated Learning for Autonomous Driving
Sonakshi Garg, Hugo Jönsson, Gustav Kalander +4
Federated Learning (FL) is a decentralized learning paradigm, enabling parties to collaboratively train models while keeping their data confidential. Within autonomous driving, it…