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Effective and Efficient Federated Tree Learning on Hybrid Data: HybridTree


Core Concepts
HybridTree, a novel federated learning approach, enables effective and efficient federated tree learning on hybrid data by incorporating the knowledge of guest parties through a layer-level solution.
Abstract
The paper proposes HybridTree, a novel federated learning approach that enables effective and efficient federated tree learning on hybrid data. Key insights: The authors observe the existence of consistent "meta-rules" in decision trees, where the prediction value is deterministic if certain split conditions are satisfied. These meta-rules represent the simple and neat knowledge contributed by guest parties. The authors propose a tree transformation technique that can reorder the split points without compromising the model performance, allowing them to incorporate the guest parties' knowledge by appending layers to the tree structure. Based on the tree transformation, the authors design HybridTree, a layer-level federated tree learning algorithm. HybridTree integrates the knowledge of guest parties by appending layers to the tree, without requiring frequent communication or complicated aggregation mechanisms. The training of HybridTree consists of three steps: The host party trains a subtree individually using its local features and labels. The host party sends the encrypted gradients of the instances in the last layer to the guest parties. The guest parties update the following lower layers of the tree using their local features and receive encrypted gradients, and send back the encrypted prediction values. The inference process of HybridTree involves collaborative efforts between the host and guest parties to predict an input instance. The experiments demonstrate that HybridTree can achieve comparable accuracy to the centralized setting while achieving up to 8 times speedup compared with other baselines.
Stats
The host party holds the synthetic transaction data and the label, while multiple guest parties hold the account data. The number of guest parties is set to 25 for the AD and DEV-AD datasets, and 5 for the Adult and Cod-rna datasets.
Quotes
"Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data." "In practical scenarios, hybrid FL is quite common, yet it has not been extensively explored in the current literature." "We observe the existence of consistent split rules in trees. With the help of these split rules, we theoretically show that the knowledge of parties can be incorporated into the lower layers of a tree."

Key Insights Distilled From

by Qinbin Li,Ch... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2310.11865.pdf
Effective and Efficient Federated Tree Learning on Hybrid Data

Deeper Inquiries

How can HybridTree be extended to handle more complex hybrid data settings, such as those with varying feature and sample spaces across parties

To extend HybridTree to handle more complex hybrid data settings with varying feature and sample spaces across parties, several modifications and enhancements can be implemented: Dynamic Split Rules: Instead of assuming consistent split rules as in the current implementation, the algorithm can be adapted to dynamically adjust split rules based on the specific features and samples available at each party. This flexibility would allow for more adaptive and accurate tree construction in diverse data settings. Meta-Rule Discovery: Enhancing the algorithm to automatically discover and incorporate meta-rules specific to each party's data characteristics can improve the efficiency and effectiveness of knowledge aggregation. This could involve advanced feature engineering techniques and meta-learning approaches. Multi-Modal Data Integration: Incorporating techniques to handle multi-modal data, where different parties contribute data in various formats (e.g., images, text, numerical data), would require adapting the tree structure and aggregation methods to accommodate diverse data types. Privacy-Preserving Mechanisms: Strengthening privacy measures to ensure secure aggregation of knowledge across parties with varying data spaces. This could involve implementing advanced encryption techniques and differential privacy mechanisms tailored to the specific data heterogeneity. By incorporating these enhancements, HybridTree can be extended to handle more complex hybrid data settings effectively and efficiently.

What are the potential privacy implications of the layer-level knowledge aggregation approach used in HybridTree, and how can it be further strengthened to provide stronger privacy guarantees

The layer-level knowledge aggregation approach used in HybridTree may have potential privacy implications, especially in federated learning scenarios where data privacy is a critical concern. To strengthen privacy guarantees, the following measures can be implemented: Enhanced Encryption: Implementing stronger encryption techniques, such as homomorphic encryption and secure multi-party computation, to protect the exchanged gradients and prediction values during the training and inference processes. This ensures that sensitive information remains confidential. Differential Privacy: Integrating differential privacy mechanisms to add noise to the gradients or prediction values, thereby preventing the leakage of individual data points while still enabling effective model training and inference. Privacy-Preserving Aggregation: Utilizing privacy-preserving aggregation techniques to combine knowledge from different parties without exposing individual data details. This can include secure aggregation protocols and cryptographic methods to ensure data confidentiality. Privacy Impact Assessment: Conducting regular privacy impact assessments to evaluate the potential privacy risks and vulnerabilities in the federated learning process. This helps in identifying and addressing privacy concerns proactively. By implementing these privacy-enhancing measures, the layer-level knowledge aggregation approach in HybridTree can be further strengthened to provide robust privacy guarantees and protect sensitive data in federated learning environments.

Given the focus on tabular data, how can the ideas behind HybridTree be adapted to handle other data modalities, such as images or text, in a federated learning setting

Adapting the ideas behind HybridTree to handle other data modalities, such as images or text, in a federated learning setting involves the following considerations: Feature Extraction: For image or text data, feature extraction techniques like convolutional neural networks (CNNs) for images and word embeddings for text can be used to convert the raw data into a format suitable for tree-based models like GBDT. Data Representation: Transforming the multi-modal data into a tabular format that can be processed by GBDT models. This may involve encoding image features as numerical values or using text embeddings to represent textual data. Model Adaptation: Modifying the tree construction and aggregation process to accommodate the unique characteristics of image or text data. This may include adjusting split criteria, handling missing values, and incorporating domain-specific knowledge. Privacy Considerations: Ensuring that privacy-preserving techniques are tailored to the specific requirements of image or text data, considering the sensitivity of visual and textual information. This may involve specialized encryption methods and privacy-enhancing protocols. By adapting HybridTree to handle diverse data modalities, federated learning systems can effectively leverage the benefits of collaborative model training across different types of data while maintaining data privacy and security.
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