1 citations · 1 across the 4 of their papers we have counts for
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★ 1 cited
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…