Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
<|reference_start|>Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning: Over the last few years, federated learning (FL) has emerged as a prominent method in machine learning, emphasizing privacy preservation by allowing multiple clients to collaboratively build a model while keeping their training data private. Despite this focus on privacy, FL models are susceptible to various attacks, including membership inference attacks (MIAs), posing a serious threat to data confidentiality. In a recent study, Rezaei \textit{et al.} revealed the existence of an accuracy-privacy trade-off in deep ensembles and proposed a few fusion strategies to overcome it. In this paper, we aim to explore the relationship between deep ensembles and FL. Specifically, we investigate whether confidence-based metrics derived from deep ensembles apply to FL and whether there is a trade-off between accuracy and privacy in FL with respect to MIA. Empirical investigations illustrate a lack of a non-monotonic correlation between the number of clients and the accuracy-privacy trade-off. By experimenting with different numbers of federated clients, datasets, and confidence-metric-based fusion strategies, we identify and analytically justify the clear existence of the accuracy-privacy trade-off.<|reference_end|>
@article{ahamed2024accuracy-privacy,
    title={Accuracy-Privacy Trade-off in the Mitigation of Membership Inference
  Attack in Federated Learning},
    author={Sayyed Farid Ahamed, Soumya Banerjee, Sandip Roy, Devin Quinn, Marc
  Vucovich, Kevin Choi, Abdul Rahman, Alison Hu, Edward Bowen, and Sachin
  Shetty},
    journal={arXiv preprint arXiv:2407.19119},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19119},
    primaryClass={cs.LG cs.AI cs.CR}
}