Privacy-Preserving Federated Primal-Dual Learning for Non-convex and Non-smooth Problems with Bidirectional Model Sparsification

Core Concepts

The authors propose two novel privacy-preserving federated primal-dual learning algorithms, DP-FedPDM and BSDP-FedPDM, to efficiently solve non-convex and non-smooth federated learning problems while considering communication efficiency and privacy protection.

Abstract

The paper presents two novel privacy-preserving federated primal-dual learning algorithms:

DP-FedPDM: A fundamental federated primal-dual algorithm that applies differential privacy to protect client privacy.

Comprehensive privacy and convergence analyses are provided, showing DP-FedPDM can achieve a ζ-stationary solution in O(1/ζ) communication rounds, which is the lowest known for non-convex problems.

BSDP-FedPDM: An extension of DP-FedPDM that incorporates bidirectional model sparsification to further reduce communication cost.

The algorithm applies top-k and rand-k sparsifiers in both uplink and downlink communications.
Several unique properties of BSDP-FedPDM are discussed, including mitigating the "curse of primal averaging" and the tradeoff between communication cost reduction and performance.

Extensive experiments on real-world datasets (Adult and MNIST) are conducted to validate the effectiveness of the proposed algorithms and the analytical results. The results demonstrate the superior performance of the proposed algorithms over state-of-the-art federated learning algorithms in terms of communication efficiency, privacy protection, and learning performance.

Stats

The paper does not provide any specific numerical data or statistics to support the key arguments. The experimental results are presented in the form of plots showing the testing accuracy and communication costs.

Quotes

None.

Key Insights Distilled From

Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification

by Yiwei Li,Chi... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2310.19558.pdf

Deeper Inquiries

How can the proposed algorithms be extended to handle heterogeneous client objectives or non-IID data distributions

To extend the proposed algorithms to handle heterogeneous client objectives or non-IID data distributions, we can introduce personalized loss functions for each client based on their specific objectives. This can be achieved by incorporating client-specific parameters or constraints into the optimization problem. Additionally, we can implement adaptive learning rates or regularization terms to account for the varying data distributions among clients. By customizing the optimization process for each client, we can effectively address the challenges posed by heterogeneous objectives and non-IID data distributions in federated learning.

What are the potential limitations or drawbacks of the bidirectional model sparsification approach, and how can they be addressed

One potential limitation of the bidirectional model sparsification approach is the trade-off between communication efficiency and model accuracy. While model sparsification can reduce the communication cost, it may lead to performance degradation due to information loss during compression. To address this limitation, we can explore adaptive sparsification techniques that dynamically adjust the compression ratio based on the model complexity or data characteristics. Additionally, incorporating advanced compression algorithms or hybrid sparsification methods can help mitigate the drawbacks of traditional sparsification techniques and improve the overall performance of the bidirectional model sparsification approach.

What other types of non-convex and non-smooth optimization problems in federated learning could benefit from the proposed primal-dual framework

The proposed primal-dual framework for non-convex and non-smooth optimization problems in federated learning can benefit various applications beyond logistic regression, such as image classification, natural language processing, and anomaly detection. These problems often involve complex, non-linear relationships and irregular data distributions, making them suitable candidates for the primal-dual optimization approach. By adapting the algorithm to handle different loss functions, regularization terms, or model architectures specific to these applications, we can effectively address a wide range of non-convex and non-smooth optimization challenges in federated learning. Additionally, incorporating domain-specific constraints or objectives into the optimization framework can further enhance the performance and applicability of the proposed primal-dual approach.