Preserving Privacy in Federated Learning on Riemannian Manifolds using Differential Privacy

Temel Kavramlar

A novel private federated learning framework on Riemannian manifolds (PriRFed) is proposed, which ensures differential privacy and provides convergence guarantees.

Özet

The paper proposes a private federated learning (FL) framework on Riemannian manifolds, named PriRFed, which aims to safeguard data privacy while solving distributed optimization problems on non-Euclidean spaces.
Key highlights:

PriRFed integrates differential privacy (DP) techniques into the FL setting on Riemannian manifolds, providing privacy guarantees for the learned model.
The framework involves two stages: private local training by agents and server-side aggregation using Riemannian optimization techniques.
For the local training, the authors analyze the convergence of PriRFed using two DP-based Riemannian optimization algorithms: DP-RSGD and DP-RSVRG.
Theoretical analysis shows that PriRFed satisfies (ε, δ)-DP, and the privacy budget does not degrade significantly with the number of iterations.
Numerical experiments on synthetic and real-world datasets demonstrate the efficacy of the proposed PriRFed approach.

İstatistikler

The paper does not provide specific numerical data or statistics. It focuses on the theoretical analysis of the proposed private federated learning framework.

Alıntılar

The paper does not contain any striking quotes that support the key arguments.

Önemli Bilgiler Şuradan Elde Edildi

Federated Learning on Riemannian Manifolds with Differential Privacy

by Zhenwei Huan... : arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10029.pdf

Daha Derin Sorular

How can the proposed PriRFed framework be extended to handle heterogeneous data distributions across agents

To extend the PriRFed framework to handle heterogeneous data distributions across agents, we can introduce data preprocessing techniques that aim to align the data distributions before training. This can involve data normalization, feature scaling, or even more advanced methods like domain adaptation or transfer learning. By preprocessing the data to make it more homogeneous across agents, we can ensure that the federated learning process is more effective and that the model learns from a more consistent dataset. Additionally, we can incorporate techniques like data augmentation or synthetic data generation to balance out the differences in data distributions and improve the overall performance of the framework.

What are the potential challenges in applying PriRFed to large-scale real-world problems, and how can they be addressed

Applying PriRFed to large-scale real-world problems may pose several challenges, such as increased computational complexity, communication overhead, and scalability issues. To address these challenges, we can implement distributed computing techniques to distribute the computational load across multiple nodes or devices. This can involve parallelizing the training process, optimizing communication protocols, and utilizing cloud computing resources. Additionally, optimizing the algorithm for efficiency, implementing model compression techniques, and using hardware accelerators like GPUs can help improve the scalability of PriRFed for large-scale problems. Regular monitoring and tuning of the system performance can also help identify and address any bottlenecks or inefficiencies in the framework.

Can the privacy-utility trade-off in PriRFed be further optimized, for example, by adaptively tuning the privacy parameters during the training process

The privacy-utility trade-off in PriRFed can be further optimized by adaptively tuning the privacy parameters during the training process. This can involve dynamically adjusting the privacy budget (ǫ) and the probability of failure (δ) based on the sensitivity of the data, the model performance, and the level of privacy required. By monitoring the model's performance metrics, such as accuracy, loss, or convergence rate, we can dynamically update the privacy parameters to strike a better balance between privacy protection and model utility. Additionally, implementing advanced privacy-preserving techniques like differential privacy with adaptive noise scaling or privacy amplification can help enhance the privacy-utility trade-off in PriRFed. Regularly evaluating and fine-tuning the privacy parameters based on the evolving data and model requirements can lead to a more optimized and effective federated learning framework.