Summary of Entity Augmentation For Efficient Classification Of Vertically Partitioned Data with Limited Overlap, by Avi Amalanshu et al.
Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap
by Avi Amalanshu, Viswesh Nagaswamy, G. V. S. S. Prudhvi, Yash Sirvi, Debashish Chakravarty
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 This paper presents a novel approach to Vertical Federated Learning (VFL), which enables machine learning models to learn from vertically partitioned data without communicating raw data. The proposed technique, Entity Augmentation, eliminates the need for traditional entity resolution and alignment steps, making VFL more efficient and privacy-preserving. By generating meaningful labels for activations sent to the host, regardless of their originating entity, the approach allows for substantial performance improvements (e.g., 48.1% vs 69.48% test accuracy on CIFAR-10 with 5% overlap). The Entity Augmentation method also exhibits a regularizing effect that leads to marginally better performance even when training data has high overlap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VFL is a way for computers to learn from lots of different pieces of information without sharing the raw data. This helps keep private things, like personal details, safe. Traditionally, this process involves identifying and matching unique items, which can be slow and not very private. The authors propose a new method called Entity Augmentation that makes VFL more efficient and private. It works by giving meaningful labels to the information sent to the main computer, without needing to match up all the different pieces of information. This leads to better performance in machine learning tasks, even when the training data doesn’t overlap much. |
Keywords
» Artificial intelligence » Alignment » Federated learning » Machine learning
