Differentially Private Distributed Nonconvex Stochastic Optimization with Quantized Communications

Kernekoncepter

Proposing an algorithm for privacy-preserving distributed nonconvex stochastic optimization with quantized communications.

Resumé

This paper introduces a novel algorithm for distributed nonconvex stochastic optimization with differential privacy. It addresses privacy protection, communication efficiency, and convergence simultaneously. The algorithm adds privacy noises and quantizes information to enhance privacy levels and reduce the impact of privacy noises. The convergence rate and differential privacy level are analyzed, showing improvements over existing methods. The paper provides a comprehensive numerical example to demonstrate the effectiveness of the proposed algorithm.

Statistik

Each node adds time-varying privacy noises to its local state.
The proposed algorithm achieves mean convergence and a finite cumulative differential privacy budget over infinite iterations.
Employing the subsampling method controlled through the sample-size parameter.

Citater

"The proposed algorithm achieves both the mean convergence and a finite cumulative differential privacy budget over infinite iterations."
"Compared with existing works, the differential privacy level is enhanced, and a finite cumulative differential privacy budget is achieved over infinite iterations."

Vigtigste indsigter udtrukket fra

Differentially Private Distributed Nonconvex Stochastic Optimization with Quantized Communications

by Jialong Chen... kl. arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18254.pdf

Dybere Forespørgsler

How does the sample-size parameter impact the convergence and privacy levels in the algorithm

In the algorithm, the sample-size parameter plays a crucial role in determining the convergence and privacy levels. A larger sample size, denoted by the parameter γ, allows for more accurate estimation of the gradients at each node. This increased accuracy can lead to faster convergence of the optimization algorithm as it makes better-informed updates to the model parameters. Additionally, a larger sample size can help reduce the impact of noise in the system, thereby improving the overall privacy level of the algorithm. By taking more data samples into account, the algorithm can better protect sensitive information and enhance the differential privacy guarantees.

What are the potential drawbacks or limitations of using quantized communications in privacy-preserving optimization algorithms

While quantized communications offer benefits such as reduced bandwidth usage and improved communication efficiency, there are potential drawbacks and limitations to consider in privacy-preserving optimization algorithms. One limitation is the loss of information fidelity due to quantization. When information is quantized before transmission, some details may be lost or distorted, potentially affecting the accuracy of the optimization process. Additionally, the choice of quantization levels and strategies can impact the overall performance of the algorithm. Improper quantization schemes may introduce additional noise or bias, leading to suboptimal convergence and privacy guarantees. It is essential to carefully design and optimize the quantization process to balance communication efficiency with information fidelity and privacy protection.

How can the proposed algorithm be applied to real-world scenarios beyond the "MNIST" dataset

The proposed algorithm for differentially private distributed nonconvex stochastic optimization with quantized communications can be applied to various real-world scenarios beyond the "MNIST" dataset. One potential application is in federated learning settings, where multiple parties collaborate to train a shared machine learning model without sharing their raw data. By incorporating differential privacy and quantized communications, the algorithm can enable secure and privacy-preserving model training across distributed nodes. This can be particularly useful in healthcare, finance, and other industries where data privacy is a critical concern. Additionally, the algorithm can be adapted for edge computing environments, enabling privacy-preserving optimization on resource-constrained devices while maintaining communication efficiency and convergence guarantees.