Balancing Privacy Protection and Interpretability in Federated Learning
Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby potentially protecting users' private information. Nevertheless, recent studies have illustrated that FL still suffers from information leakage as adversaries try to recover the training data by analyzing shared parameters from local clients. To deal with this issue, differential privacy (DP) is adopted to add noise to the gradients of local models before aggregation. It, however, results in the poor performance of gradient-based interpretability methods, since some weights capturing the salient region in feature map will be perturbed. To overcome this problem, we propose a simple yet effective adaptive differential privacy (ADP) mechanism that selectively adds noisy perturbations to the gradients of client models in FL. We also theoretically analyze the impact of gradient perturbation on the model interpretability. Finally, extensive experiments on both IID and Non-IID data demonstrate that the proposed ADP can achieve a good trade-off between privacy and interpretability in FL.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningSimilar Papers 제목 키워드 기반
Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques
In federated learning (FL), balancing privacy protection, learning quality, and efficiency remains a challenge. Privacy protection mechanisms, such as Differential Privacy (DP), degrade learning quality, or, as in the ca…
Federated LearningTheoretically Principled Federated Learning for Balancing Privacy and Utility
We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility. The algorithm is applicable to arb…
Federated LearningTowards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. While these challenges have been widely disc…
Federated LearningPrivacy PreservingFedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection
Federated learning (FL) offers an innovative paradigm for collaborative model training across decentralized devices, such as smartphones, balancing enhanced predictive performance with the protection of user privacy in s…
Federated LearningTowards Case-based Interpretability for Medical Federated Learning
We explore deep generative models to generate case-based explanations in a medical federated learning setting. Explaining AI model decisions through case-based interpretability is paramount to increasing trust and allowi…
Federated Learning