@inproceedings{475a93129c564861a7aceb9d52d87236,
title = "Evaluating Differential Privacy in Federated Continual Learning",
abstract = "In recent years, the privacy-protecting framework Differential Privacy (DP) has achieved remarkable success and has been widely studied. However, there is a lack of work on DP in the area of Federated Continual Learning (FCL), which is a combination of Federated Learning (FL) and Continual Learning (CL). This paper presents a formal definition of DP-FCL and evaluates several DP-FCL methods based on Gaussian DP (GDP) and Individual DP (IDP). The experimental results indicate that gradient modification based CL strategies are not practical in DP-FCL. To the best of our knowledge, this is the first work to experimentally study DP-FCL, which can provide a reference for future research in this area.",
keywords = "continual learning, deep learning, differential privacy, federated continual learning, federated learning, privacy",
author = "Junyan Ouyang and Rui Han and Liu, {Chi Harold}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 98th IEEE Vehicular Technology Conference, VTC 2023-Fall ; Conference date: 10-10-2023 Through 13-10-2023",
year = "2023",
doi = "10.1109/VTC2023-Fall60731.2023.10333463",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings",
address = "United States",
}