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cs.CR2025
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias Åkesson, Johan Östman +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…
cs.CR2022
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…