Fedsel: Federated sgd under local differential privacy with top-k dimension selection

R Liu, Y Cao, M Yoshikawa, H Chen - … 24–27, 2020, Proceedings, Part I 25, 2020 - Springer
As massive data are produced from small gadgets, federated learning on mobile devices
has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) …

LDP-FL: Practical private aggregation in federated learning with local differential privacy

L Sun, J Qian, X Chen - arXiv preprint arXiv:2007.15789, 2020 - arxiv.org
Train machine learning models on sensitive user data has raised increasing privacy
concerns in many areas. Federated learning is a popular approach for privacy protection …

Federated learning with local differential privacy: Trade-offs between privacy, utility, and communication

M Kim, O Günlü, RF Schaefer - ICASSP 2021-2021 IEEE …, 2021 - ieeexplore.ieee.org
Federated learning (FL) allows to train a massive amount of data privately due to its
decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to …

[HTML][HTML] Safeguarding cross-silo federated learning with local differential privacy

C Wang, X Wu, G Liu, T Deng, K Peng… - Digital Communications …, 2022 - Elsevier
Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine
Learning (ML), where the ML model is trained in a decentralized manner by the clients …

Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy

R Hu, Y Guo, Y Gong - IEEE Transactions on Mobile Computing, 2023 - ieeexplore.ieee.org
Federated learning (FL) that enables edge devices to collaboratively learn a shared model
while keeping their training data locally has received great attention recently and can protect …

Dynamic personalized federated learning with adaptive differential privacy

X Yang, W Huang, M Ye - Advances in Neural Information …, 2023 - proceedings.neurips.cc
Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

Flame: Differentially private federated learning in the shuffle model

R Liu, Y Cao, H Chen, R Guo… - Proceedings of the AAAI …, 2021 - ojs.aaai.org
Federated Learning (FL) is a promising machine learning paradigm that enables the
analyzer to train a model without collecting users' raw data. To ensure users' privacy …

Collecting and analyzing multidimensional data with local differential privacy

N Wang, X Xiao, Y Yang, J Zhao, SC Hui… - 2019 IEEE 35th …, 2019 - ieeexplore.ieee.org
Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …

Clustered federated learning with adaptive local differential privacy on heterogeneous iot data

Z He, L Wang, Z Cai - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
The Internet of Things (IoT) is penetrating many aspects of our daily life with the proliferation
of artificial intelligence applications. Federated learning (FL) has emerged as a promising …

Providing input-discriminative protection for local differential privacy

X Gu, M Li, L Xiong, Y Cao - 2020 IEEE 36th International …, 2020 - ieeexplore.ieee.org
Local Differential Privacy (LDP) provides provable privacy protection for data collection
without the assumption of the trusted data server. In the real-world scenario, different data …