Mitigating Disparate Impact of Differential Privacy in Federated Learning through Robust Clustering

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Mitigating Disparate Impact of Differential Privacy in Federated Learning through Robust Clustering

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Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning

S Malekmohammadi, Y Yu, Y Cao - arXiv preprint arXiv:2406.03519, 2024 - arxiv.org

High utility and rigorous data privacy are of the main goals of a federated learning (FL)
system, which learns a model from the data distributed among some clients. The latter has …

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Mitigating Disparate Impact of Differential Privacy in Federated Learning through Robust Clustering

Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning

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