Summary of A Bargaining-based Approach For Feature Trading in Vertical Federated Learning, by Yue Cui et al.
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning
by Yue Cui, Liuyi Yao, Zitao Li, Yaliang Li, Bolin Ding, Xiaofang Zhou
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 The paper proposes a novel approach to Vertical Federated Learning (VFL) feature trading, aiming to encourage economically efficient transactions between task and data parties. By incorporating performance gain-based pricing, the model optimizes revenue-based objectives for both parties, considering the importance of VFL in preserving data privacy. The proposed bargaining-based feature trading approach is analyzed under perfect and imperfect performance information settings, demonstrating the existence of an equilibrium that optimizes party objectives. Additionally, the authors develop performance gain estimation-based bargaining strategies for imperfect performance scenarios and discuss potential security issues and solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers created a new way to trade features in Vertical Federated Learning (VFL). This helps data parties get paid fairly for their information while task parties benefit from better results. The model considers how well the traded features perform and tries to find an agreement that works for both sides. This is important because VFL allows training models on different types of data without sharing sensitive info. The study shows how this approach can work in different situations and presents strategies for when there’s not perfect information about performance. |
Keywords
* Artificial intelligence * Federated learning
