Constrained Multi-Objective Optimization of Hyperparameters for SecureBoost to Balance Utility, Efficiency, and Privacy in Vertical Federated Learning

Core Concepts

The core message of this paper is to propose the Constrained Multi-Objective SecureBoost (CMOSB) algorithm, which identifies Pareto optimal solutions of hyperparameters that simultaneously minimize utility loss, training cost, and privacy leakage in vertical federated learning.

Abstract

The paper addresses two main limitations of the SecureBoost algorithm: privacy leakage and suboptimal hyperparameter configuration.

Privacy Leakage: The authors propose a novel label inference attack called Instance Clustering Attack (ICA) that can exploit the unprotected instance distributions in SecureBoost to infer the labels of the active party. To mitigate this vulnerability, the authors develop two defense methods: local trees and purity threshold.

Hyperparameter Optimization: The authors formulate the Constrained Multi-Objective SecureBoost (CMOSB) problem, which aims to find Pareto optimal solutions of hyperparameters that achieve an optimal trade-off between utility loss, training cost, and privacy leakage. The CMOSB algorithm is proposed to solve this problem, leveraging NSGA-II to efficiently identify the Pareto front.

The experimental results demonstrate that the Pareto optimal solutions found by CMOSB outperform the baseline methods (Empirical Selection, Grid Search, and Bayesian Optimization) in terms of the trade-off between the three objectives. The authors also show that CMOSB can find better Pareto optimal solutions when constraints are applied on the objectives, ensuring the solutions satisfy the specific requirements of VFL participants.

Stats

The training cost is estimated by summing the time spent on homomorphic encryption operations, including encryption, decryption, and addition.
The privacy leakage is measured by the accuracy of the proposed Instance Clustering Attack (ICA).

Quotes

"SecureBoost still faces the possibility of label leakage [5] through intermediate information, despite employing homomorphic encryption to protect instance gradients."
"Heuristic hyperparameter configuration may lead to suboptimal trade-off between utility, efficiency, and privacy of the SecureBoost model."

Key Insights Distilled From

Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning

by Yan Kang,Ziy... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04490.pdf

Deeper Inquiries

How can the CMOSB algorithm be extended to optimize other objectives beyond utility, efficiency, and privacy, such as fairness or robustness, in the context of vertical federated learning

The CMOSB algorithm can be extended to optimize other objectives beyond utility, efficiency, and privacy by incorporating additional constraints and objectives into the optimization process. To optimize for fairness, one could introduce constraints related to bias or discrimination in the model predictions. This could involve measuring and minimizing disparate impact or ensuring demographic parity in the model outcomes.
For robustness, the algorithm could be extended to include constraints related to model stability and generalization. This could involve optimizing for robustness to adversarial attacks or ensuring that the model performs consistently across different datasets or environments.
In the context of vertical federated learning, these additional objectives can be incorporated into the multi-objective optimization framework of CMOSB. By defining appropriate constraints and objectives, the algorithm can find Pareto optimal solutions that balance utility, efficiency, privacy, fairness, and robustness in a federated learning setting.

What are the potential limitations or drawbacks of the proposed defense methods (local trees and purity threshold) in terms of their impact on model performance or training efficiency

While the local trees and purity threshold defense methods are effective in mitigating privacy leakage, they may have potential limitations or drawbacks that could impact model performance or training efficiency.

Impact on Model Performance:

Local Trees: Training a subset of decision trees locally before federated learning may lead to a loss of model performance. The locally trained trees may not capture the full complexity of the data, potentially reducing the overall utility of the model.
Purity Threshold: Setting a purity threshold for node splitting may result in suboptimal splits in the tree, affecting the model's predictive power. If the threshold is too strict, it could lead to underfitting, while a lenient threshold may result in overfitting.

Training Efficiency:

Local Trees: Training additional trees locally can increase the computational overhead and training time, impacting the efficiency of the federated learning process.
Purity Threshold: Checking the purity of nodes and splitting locally based on the threshold may introduce additional computational complexity, potentially slowing down the training process.

Sensitivity to Hyperparameters:

Both defense methods rely on hyperparameters such as the number of locally trained trees and the purity threshold. Tuning these hyperparameters effectively can be challenging and may require manual intervention, impacting the scalability and automation of the defense mechanisms.

How can the CMOSB algorithm be adapted to handle dynamic or evolving constraints on the objectives, such as changing privacy requirements or resource constraints, during the federated learning process

To adapt the CMOSB algorithm to handle dynamic or evolving constraints during the federated learning process, several strategies can be employed:

Dynamic Constraint Updating:

Implement a mechanism to dynamically update the constraints based on changing privacy requirements or resource constraints. This could involve monitoring the system performance and adjusting the constraints in real-time.

Constraint Flexibility:

Design the algorithm to be flexible in handling varying constraints by allowing for the addition or modification of constraints during the optimization process. This flexibility can accommodate evolving privacy regulations or changing resource availability.

Constraint Learning:

Integrate machine learning techniques to learn and adapt to changing constraints over time. This could involve using reinforcement learning or online learning algorithms to optimize the objectives while adhering to dynamic constraints.

Constraint Trade-offs:

Enable the algorithm to make trade-offs between objectives and constraints based on their relative importance. This could involve assigning weights to different constraints or objectives and optimizing the overall utility while satisfying the constraints to the best extent possible.