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Gradient-less Federated XGBoost with Learnable Learning Rates for Privacy and Efficiency


Core Concepts
Developing a privacy-preserving framework for horizontal federated XGBoost without sharing gradients, improving communication efficiency.
Abstract
Introduction: Discusses the need for training XGBoost in federated learning due to privacy concerns. Existing Works: Focus on neural networks in FL, limited exploration for other ML models like XGBoost. Challenges: Horizontal vs. vertical settings in federated XGBoost, difficulties in optimal split conditions. Proposed Solution: Introduces FedXGBllr framework with learnable learning rates to address privacy and communication efficiency. Methodology: Formulates intuitions, facilitates them through a one-layer 1D CNN, and develops the FedXGBllr framework. Experiments: Extensive evaluations show comparable performance to state-of-the-art methods and reduced communication overhead by factors ranging from 25x to 700x. Results: Outperforms or matches accuracy of SimFL and centralized baselines on classification datasets; achieves comparable MSE on regression datasets. Ablation Studies: Demonstrates interpretability of the one-layer 1D CNN model coupled with high performance. Communication Overhead Comparison: Significantly lower communication overhead compared to SimFL, saving costs by factors ranging from 25x to 700x.
Stats
"Our approach achieves performance comparable to the state-of-the-art method." "Effectively improves communication efficiency by lowering both communication rounds and overhead by factors ranging from 25x to 700x."
Quotes

Deeper Inquiries

How can the FedXGBllr framework be extended to support vertical federated learning

Vertical federated learning involves scenarios where the feature spaces across different clients are not identical, unlike in horizontal federated learning. To extend the FedXGBllr framework to support vertical federated learning, we can introduce modifications to handle this heterogeneity. One approach could be to adapt the aggregation process to account for varying feature spaces by incorporating alignment techniques or transformations before aggregating the tree ensembles from different clients. Additionally, we may need to adjust the model architecture or training process to accommodate these differences and ensure effective collaboration among clients with diverse data distributions.

What counterarguments exist against the use of learnable learning rates in federated XGBoost

Counterarguments against using learnable learning rates in federated XGBoost may include concerns about model complexity and potential overfitting. Introducing learnable parameters for each tree ensemble could increase the number of hyperparameters that need tuning, leading to a more intricate optimization process. Moreover, there is a risk of overfitting if the learnable learning rates capture noise or idiosyncrasies specific to individual client datasets rather than general patterns. This could result in reduced model generalization and performance degradation on unseen data.

How might interpretability impact the adoption of privacy-preserving frameworks in machine learning

Interpretability plays a crucial role in enhancing trust and understanding of privacy-preserving frameworks in machine learning. By providing transparency into how decisions are made and ensuring that models adhere to ethical standards, interpretability can foster greater acceptance and adoption of such frameworks among stakeholders. It allows users to verify that sensitive information is adequately protected and helps build confidence in the reliability of privacy-preserving mechanisms. Additionally, interpretable models facilitate compliance with regulations governing data privacy by enabling clear explanations of how privacy measures are implemented within machine learning systems.
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