Leveraging channel noise for sampling and privacy via quantized federated langevin monte carlo

Y Zhang, D Liu, O Simeone - 2022 IEEE 23rd International …, 2022 - ieeexplore.ieee.org
For engineering applications of artificial intelligence, Bayesian learning holds significant
advantages over standard frequentist learning, including the capacity to quantify uncertainty …
被引用次数：4 相关文章

Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo

Y Zhang, D Liu, O Simeone - arXiv preprint arXiv:2202.13932, 2022 - arxiv.org
For engineering applications of artificial intelligence, Bayesian learning holds significant
advantages over standard frequentist learning, including the capacity to quantify uncertainty …

Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo

Y Zhang, D Liu, O Simeone - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
For engineering applications of artificial intelligence, Bayesian learning holds significant
advantages over standard frequentist learning, including the capacity to quantify uncertainty …

Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo

Y Zhang, D Liu, O Simeone - 23rd IEEE International …, 2022 - researchwith.njit.edu
For engineering applications of artificial intelligence, Bayesian learning holds significant
advantages over standard frequentist learning, including the capacity to quantify uncertainty …

Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo

Y Zhang, D Liu, O Simeone - 23rd IEEE International Workshop on …, 2022 - kclpure.kcl.ac.uk
For engineering applications of artificial intelligence, Bayesian learning holds significant
advantages over standard frequentist learning, including the capacity to quantify uncertainty …