Demonstrates a method that enhances global model security in federated learning by verifying data through trusted nodes, indicating a solution to adversarial threats.
The problem of ensuring the security of a global computational model in federated learning systems is considered. A method is proposed that is based on data verification using a trusted group of nodes and ensures that only correct updates are taken into account during the global model aggregation process. It is experimentally demonstrated that the developed method ensures accurate identification and isolation of adversaries implementing label-flipping and noise-injection threats.
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Krundyshev et al. (2025) studied this question.
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