FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning

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 sh...

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Bibliographic Details
Published inElectronics (Basel) Vol. 13; no. 21; p. 4187
Main Authors Liu, Teng, Wu, Hao, Sun, Xidong, Niu, Chaojie, Yin, Hao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2024
Subjects
Accuracy
Algorithms
Balancing
Communication
Customization
Data integrity
Federated learning
Machine learning
Optimization techniques
Privacy
Privacy, Right of
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Summary: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. The 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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13214187