Summary of A Collaborative Ensemble Construction Method For Federated Random Forest, by Penjan Antonio Eng Lim and Cheong Hee Park
A collaborative ensemble construction method for federated random forest
by Penjan Antonio Eng Lim, Cheong Hee Park
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel federated random forest approach is proposed, which addresses the challenges of training tree-based models in decentralized settings. The method employs a unique ensemble construction technique that iteratively grows decision trees across clients to improve performance under non-identically distributed (non-IID) data. To preserve privacy, leaf nodes store only majority class labels from local client data, limiting information disclosure. This collaborative approach enhances the ensemble’s ability to capture heterogeneity in client data, leading to better performance on non-IID datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated random forests can train models across multiple devices while keeping each device’s data private. This is useful when devices have different types of data. The paper presents a new way to build these federated random forests that works well even when the data is not identical between devices. To keep the data private, only the majority class label from each device’s data is stored in the leaves. This approach helps create an ensemble that can handle different types of data and improves its performance. |
Keywords
* Artificial intelligence * Random forest
