Mitigating gradient inversion attacks in federated learning with frequency transformation.
(2024)
Conference Proceeding
PALIHAWADANA, C., WIRATUNGA, N., KALUTARAGE, H. and WIJEKOON, A. 2024. Mitigating gradient inversion attacks in federated learning with frequency transformation. In Katsikas, S. et al. (eds.) Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops), 25-29 September 2023, The Hague, Netherlands. Lecture notes in computer science, 14399. Cham: Springer [online], part II, pages 750-760. Available from: https://doi.org/10.1007/978-3-031-54129-2_44
Centralised machine learning approaches have raised concerns regarding the privacy of client data. To address this issue, privacy-preserving techniques such as Federated Learning (FL) have emerged, where only updated gradients are communicated instea... Read More about Mitigating gradient inversion attacks in federated learning with frequency transformation..