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Found 3 papers, 1 papers with code
Poisoning Attacks on Federated Learning for Autonomous Driving
FLStealth, an untargeted attack, aims at providing model updates that deteriorate the global model performance while appearing benign.
FedVal: Different good or different bad in federated learning
In this paper, we present a novel approach FedVal for both robustness and fairness that does not require any additional information from clients that could raise privacy concerns and consequently compromise the integrity of the FL system.
Detection and Prevention Against Poisoning Attacks in Federated Learning
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 via average accuracy deviation detection (AADD).
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