FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
<|reference_start|>FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation: Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific conditions, has triggered numerous follow-up research in designing powerful attacking methods and effective defending mechanisms aiming to thwart these attacking methods. Nevertheless, privacy-preserving mechanisms employed in these defending methods invariably lead to compromised model performances due to a fixed obfuscation applied to private data or gradients. In this article, we, therefore, propose a novel adaptive obfuscation mechanism, coined FedAdOb, to protect private data without yielding original model performances. Technically, FedAdOb utilizes passport-based adaptive obfuscation to ensure data privacy in both horizontal and vertical federated learning settings. The privacy-preserving capabilities of FedAdOb, specifically with regard to private features and labels, are theoretically proven through Theorems 1 and 2. Furthermore, extensive experimental evaluations conducted on various datasets and network architectures demonstrate the effectiveness of FedAdOb by manifesting its superior trade-off between privacy preservation and model performance, surpassing existing methods.<|reference_end|>
@article{gu2024fedadob:,
    title={FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive
  Obfuscation},
    author={Hanlin Gu, Jiahuan Luo, Yan Kang, Yuan Yao, Gongxi Zhu, Bowen Li,
  Lixin Fan and Qiang Yang},
    journal={arXiv preprint arXiv:2406.01085},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01085},
    primaryClass={cs.CR cs.AI}
}