Version 1
: Received: 27 September 2024 / Approved: 29 September 2024 / Online: 29 September 2024 (07:32:49 CEST)
How to cite:
Liu, T.; Wu, H.; Sun, X.; Niu, C.; Yin, H. FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning. Preprints2024, 2024092292. https://doi.org/10.20944/preprints202409.2292.v1
Liu, T.; Wu, H.; Sun, X.; Niu, C.; Yin, H. FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning. Preprints 2024, 2024092292. https://doi.org/10.20944/preprints202409.2292.v1
Liu, T.; Wu, H.; Sun, X.; Niu, C.; Yin, H. FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning. Preprints2024, 2024092292. https://doi.org/10.20944/preprints202409.2292.v1
APA Style
Liu, T., Wu, H., Sun, X., Niu, C., & Yin, H. (2024). FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning. Preprints. https://doi.org/10.20944/preprints202409.2292.v1
Chicago/Turabian Style
Liu, T., Chaojie Niu and Hao Yin. 2024 "FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning" Preprints. https://doi.org/10.20944/preprints202409.2292.v1
Abstract
Federated Learning (FL), as a distributed machine learning method, is particularly suitable for training models that require large amounts of data while meeting increasingly strict data privacy and security requirements. Although FL effectively protects the privacy of participants by avoiding the sharing of raw data, balancing the risks of privacy leakage with model performance remains a significant challenge. To address this, this paper proposes a new algorithm—FL-APB (Federated Learning with Adversarial Privacy-Performance Balancing). This algorithm combines adversarial training with privacy protection mechanisms to dynamically adjust privacy and performance budgets, optimizing the balance between the two while enhancing and ensuring performance. Experimental results demonstrate that the FL-APB algorithm significantly improves model performance across various adversarial training scenarios, while effectively protecting the privacy of participants through adversarial training of privacy data.
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
