TEAR: Exploring temporal evolution of adversarial robustness for membership inference attacks against federated learning
Federated learning (FL) is a privacy-preserving machine learning paradigm that enables
multiple clients to train a unified model without disclosing their private data. However …
multiple clients to train a unified model without disclosing their private data. However …
TEAR: Exploring Temporal Evolution of Adversarial Robustness for Membership Inference Attacks Against Federated Learning
G Liu, Z Tian, J Chen, C Wang, J Liu - IEEE Transactions on Information …, 2023 - dl.acm.org
Federated learning (FL) is a privacy-preserving machine learning paradigm that enables
multiple clients to train a unified model without disclosing their private data. However …
multiple clients to train a unified model without disclosing their private data. However …
[PDF][PDF] TEAR: Exploring Temporal Evolution of Adversarial Robustness for Membership Inference Attacks Against Federated Learning
G Liu, Z Tian, J Chen, C Wang, J Liu - IEEE Transactions on …, 2023 - drive.google.com
Manuscript received 17 October 2022; revised 28 March 2023, 4 May 2023, and 29 June
2023; accepted 5 August 2023. Date of publication 9 August 2023; date of current version 18 …
2023; accepted 5 August 2023. Date of publication 9 August 2023; date of current version 18 …