Summary of Vertical Federated Learning For Effectiveness, Security, Applicability: a Survey, by Mang Ye et al.
Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey
by Mang Ye, Wei Shen, Bo Du, Eduard Snezhko, Vassili Kovalev, Pong C. Yuen
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 comprehensive survey on Vertical Federated Learning (VFL), a privacy-preserving distributed learning paradigm that enables multiple parties to collaborate without sharing private data. The authors provide an overview of recent developments in VFL, discussing the general training protocol and analyzing limitations from three fundamental perspectives: effectiveness, security, and applicability. The paper also identifies critical future research directions, aiming to advance the field of VFL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vertical Federated Learning (VFL) is a new way for different groups to work together on projects without sharing sensitive information. This type of collaboration has shown promising results in real-world applications, but there isn’t much organization or structure to guide further research. To help move things forward, this survey provides an overview of recent developments and identifies areas where more work needs to be done. |
Keywords
* Artificial intelligence * Federated learning
