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The document introduces L OKI, a novel attack on federated learning that enables large-scale data reconstruction from multiple clients even when secure aggregation is employed. This attack significantly outperforms previous methods, achieving a data leakage rate of 76-86% in a single training round, compared to less than 1% in prior works. L OKI utilizes customized convolutional parameters to separate client gradients, allowing the malicious server to identify and reconstruct individual client data effectiv

Author

pavipvt69

Language

EN