LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy (2007.15789v2)

Abstract: Train machine learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of real data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous works do not give a practical solution due to three issues. First, the noisy data is close to its original value with high probability, increasing the risk of information exposure. Second, a large variance is introduced to the estimated average, causing poor accuracy. Last, the privacy budget explodes due to the high dimensionality of weights in deep learning models. In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues. It is capable of making the data more distinct from its original value and introducing lower variance. Moreover, the proposed mechanism bypasses the curse of dimensionality by splitting and shuffling model updates. A series of empirical evaluations on three commonly used datasets, MNIST, Fashion-MNIST and CIFAR-10, demonstrate that our solution can not only achieve superior deep learning performance but also provide a strong privacy guarantee at the same time.

• The paper introduces an adaptive LDP mechanism that optimizes perturbation for varying model weight distributions, reducing estimation variance.
• It proposes a parameter shuffling technique to disrupt weight correlations and prevent privacy leaks over multiple model updates.
• Empirical validation on datasets like MNIST demonstrates the approach achieves competitive accuracy with a low privacy budget.

An Expert Overview of "LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy"

Federated Learning (FL) has emerged as a promising approach for training machine learning models on decentralized data, notably for applications involving sensitive information such as healthcare. This paradigm shifts the focus from data centralization to local model training, thus enhancing privacy protection. However, the transmission of model updates (gradients or weights) still poses privacy risks, which necessitates advanced privacy-preservation techniques such as Local Differential Privacy (LDP).

The paper "LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy" introduces a sophisticated method for integrating LDP into FL systems. The authors, Lichao Sun, Jianwei Qian, and Xun Chen, propose a novel approach that addresses prevalent challenges in applying LDP to the federated learning context.

Key Contributions

1. Adaptive Range Differential Privacy Mechanism: This paper identifies the limitation of existing LDP methods, which often assume a fixed range for model weights across different layers of a deep neural network (DNN). This assumption introduces significant variance in model weight estimation, leading to performance degradation. The authors propose an adaptive range mechanism that optimizes the perturbation process by adjusting to the varying distributions of weights in different layers, significantly improving model accuracy.
2. Parameter Shuffling Mechanism: To mitigate privacy degradation from high dimensionality and numerous query iterations in DNN models, the authors introduce a parameter shuffling technique. This method disrupts weight correlations, preventing reconstruction attacks even when multiple updates are shared. By adopting randomized latency in reporting client updates, the mechanism ensures that the privacy budget does not accumulate across iterations.

Empirical Validation and Results

The paper presents empirical evaluations on three standard datasets: MNIST, Fashion-MNIST, and CIFAR-10. These experiments demonstrate that the proposed LDP-FL framework achieves competitive model accuracy with reduced privacy budgets compared to existing methods. Specifically, an accuracy loss of only 0.97% on MNIST with a privacy budget of $\epsilon = 1$, and similar performances on other datasets, indicates the practical viability of the approach in real-world applications.

Theoretical Implications and Future Directions

The authors offer a thorough theoretical analysis of their methods, providing proofs for privacy guarantees and bounding variance in the estimated weights. This foundational work outlines a pivotal shift in understanding LDP applications for complex models, suggesting that customizable mechanisms, such as adaptive range settings, could pave the way for more robust privacy-preserving techniques in deep learning.

In terms of future developments, while this paper significantly enhances privacy in FL, ongoing research may focus on further optimizing the balance between privacy costs and utility, exploring adaptive methods that could dynamically adjust privacy parameters based on model and data characteristics.

Conclusion

LDP-FL represents a practical advancement in federated learning, addressing core privacy concerns with innovative algorithmic solutions. This paper enriches the landscape of privacy-preserving machine learning, providing a foundation for subsequent research and application in sensitive domains like healthcare and finance, where data privacy remains paramount. As federated learning continues to evolve, integration of adaptive mechanisms and shuffling techniques as proposed in this framework could redefine privacy standards in AI systems.