Federated learning with differential privacy: Algorithms and performance analysis

K Wei, J Li, M Ding, C Ma, HH Yang… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …
被引用次数：1537 相关文章

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang… - IEEE Transactions …, 2020 - collaborate.princeton.edu
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …
[PDF] ieee.org

[PDF][PDF] Federated Learning with Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang, F Farokhi, S Jin… - ieeexplore.ieee.org
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang, F Farhad… - arXiv preprint arXiv …, 2019 - arxiv.org
In this paper, to effectively prevent information leakage, we propose a novel framework
based on the concept of differential privacy (DP), in which artificial noises are added to the …

[引用][C] Federated Learning With Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang, F Farokhi… - IEEE Transactions on …, 2020 - cir.nii.ac.jp
Federated Learning With Differential Privacy: Algorithms and Performance Analysis | CiNii
Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索フォームへ移動 …

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang, F Farhad… - arXiv e …, 2019 - ui.adsabs.harvard.edu
In this paper, to effectively prevent information leakage, we propose a novel framework
based on the concept of differential privacy (DP), in which artificial noises are added to the …

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang, F Farokhi… - IEEE Transactions on …, 2020 - dl.acm.org
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …
[PDF] deepiiotlab.com

[PDF][PDF] Federated Learning With Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang… - IEEE …, 2020 - deepiiotlab.com
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang… - IEEE Transactions …, 2020 - oar.princeton.edu
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

K Wei, J Li, M Ding, C Ma, HH Yang… - IEEE Transactions …, 2020 - researchoutput.ncku.edu.tw
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …