Summary of An Auction-based Marketplace For Model Trading in Federated Learning, by Yue Cui et al.
An Auction-based Marketplace for Model Trading in Federated Learning
by Yue Cui, Liuyi Yao, Yaliang Li, Ziqian Chen, Bolin Ding, Xiaofang Zhou
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
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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 proposes a novel approach to federated learning (FL), framing it as a marketplace where clients buy and sell models. The authors introduce an auction-based solution to ensure proper pricing based on performance gain, with incentive mechanisms to encourage truthful model valuations. They also propose a reinforcement learning framework for marketing operations to achieve maximum trading volumes. Experimental results on four datasets show that the proposed FL market can achieve high trading revenue and fair downstream task accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for many devices to work together to create better models without sharing their data. But, it’s hard to know how valuable each device’s contribution is. This paper makes FL like an online marketplace where devices buy and sell models. It’s like a trading card game! The authors came up with a special pricing system that makes sure everyone gets a fair deal. They also created a way for the market to change and adapt as more devices join in. Tests on four different datasets show that this new approach can be very effective. |
Keywords
* Artificial intelligence * Federated learning * Reinforcement learning
