1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias Åkesson, Lucas Beerens +4
Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable exp…
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…
Detection and Prevention Against Poisoning Attacks in Federated Learning
Viktor Valadi, Madeleine Englund, Mark Spanier +1
This paper proposes and investigates a new approach for detecting and preventing several different types of poisoning attacks from affecting a centralized Federated Learning model…