Efficient Vertical Federated Learning with Multiple Heads and ADMM-based Optimization

Core Concepts

The core message of this work is to propose an efficient and effective Vertical Federated Learning (VFL) optimization framework with multiple heads (VIM) that leverages the Alternating Direction Method of Multipliers (ADMM) to reduce communication costs and improve performance under differential privacy.

Abstract

The content describes a novel VFL framework called VIM that addresses the main challenges faced by existing VFL frameworks. The key highlights are: VIM introduces multiple heads in the server model, where each head corresponds to one local client. This enables a thorough decomposition of the VFL optimization problem into multiple subproblems that can be iteratively solved by the server and the clients. The authors propose an ADMM-based method called VIMADMM to solve the VIM optimization problem. VIMADMM allows clients to conduct multiple local updates before communication, which reduces the communication cost and leads to better performance under differential privacy. The authors provide theoretical analysis on the convergence of VIMADMM and prove that it can converge to stationary points under mild assumptions. To protect the privacy of local features held by clients, the authors introduce client-level differential privacy mechanisms and prove the privacy guarantees. Extensive experiments on four diverse datasets show that VIMADMM and its variant VIMADMM-J outperform state-of-the-art VFL methods in terms of faster convergence, higher accuracy, and higher utility under client-level differential privacy and label differential privacy. The authors also demonstrate that a byproduct of VIM is that the weights of learned heads reflect the importance of local clients, enabling functionalities such as client-level explanation, client denoising, and client summarization.

Stats

The number of communication rounds required for VIMADMM to converge is significantly fewer than the baselines on all four datasets. VIMADMM achieves higher test accuracy compared to the baselines on all four datasets.

Quotes

"To solve the above challenges, in this work, we propose an efficient VFL optimization framework with multiple heads (VIM), where each head corresponds to one local client." "We provide the client-level DP mechanism for our VIM framework to protect user privacy." "We conduct extensive evaluations and show that on four vertical FL datasets, VIM achieves significantly higher performance and faster convergence compared with the state-of-the-art."

Key Insights Distilled From

Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM

by Chulin Xie,P... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2207.10226.pdf

Deeper Inquiries

How can the VIM framework be extended to handle more complex model architectures beyond linear heads

The VIM framework can be extended to handle more complex model architectures beyond linear heads by incorporating non-linear transformations in the heads. Instead of restricting each head to be a linear model, we can allow each head to be a non-linear model that can capture more intricate patterns and relationships in the data. This extension would involve modifying the optimization objective in VIM to accommodate non-linear transformations and updating the ADMM-based method to handle the complexities introduced by non-linear heads. By incorporating non-linear transformations, the VIM framework can better capture the nuances in the data and improve the overall performance of the federated learning process.

What are the potential limitations or drawbacks of the client-level differential privacy approach used in this work, and how could they be addressed

One potential limitation of the client-level differential privacy approach used in this work is the trade-off between privacy and utility. Introducing noise and clipping to protect the privacy of individual clients' data may impact the accuracy and effectiveness of the model. To address this limitation, techniques such as differential privacy budget management, adaptive privacy mechanisms, and advanced privacy-preserving algorithms can be explored. Additionally, optimizing the parameters of the privacy mechanisms based on the specific requirements of the VFL task and dataset can help mitigate the impact on utility while maintaining a high level of privacy protection.

What are the broader implications of being able to quantify the importance of individual clients in a VFL setting, and how could this be leveraged in real-world applications

The ability to quantify the importance of individual clients in a VFL setting has significant implications for personalized and adaptive machine learning applications. By understanding the contribution of each client to the overall model performance, organizations can tailor their training strategies, resource allocation, and model updates to prioritize clients with higher importance. This can lead to more efficient and effective federated learning processes, improved model accuracy, and better utilization of distributed resources. In real-world applications, this quantification of client importance can be leveraged to offer personalized services, targeted recommendations, and customized solutions based on the unique contributions of each client.