most citedThe Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

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The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy

Jiating Ma, Yipeng Zhou, Qi Li +3

To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To…

cs.LG2024

Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training

Huitong Jin, Yipeng Zhou, Quan Z. Sheng +2

Differentially Private Federated Learning (DPFL) strengthens privacy protection by perturbing model gradients with noise, though at the cost of reduced accuracy. Although prior emp…