Computer Science > Machine Learning
arXiv:2402.01802 (cs)
[Submitted on 2 Feb 2024]
Title:An Auction-based Marketplace for Model Trading in Federated Learning
View a PDF of the paper titled An Auction-based Marketplace for Model Trading in Federated Learning, by Yue Cui and 5 other authors
View PDFHTML (experimental)Abstract:Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. However, the proper valuation of shared data in this collaborative process remains insufficiently addressed. In this work, we frame FL as a marketplace of models, where clients act as both buyers and sellers, engaging in model trading. This FL market allows clients to gain monetary reward by selling their own models and improve local model performance through the purchase of others' models. We propose an auction-based solution to ensure proper pricing based on performance gain. Incentive mechanisms are designed to encourage clients to truthfully reveal their model valuations. Furthermore, we introduce a reinforcement learning (RL) framework for marketing operations, aiming to achieve maximum trading volumes under the dynamic and evolving market status. Experimental results on four datasets demonstrate that the proposed FL market can achieve high trading revenue and fair downstream task accuracy.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT) |
| Cite as: | arXiv:2402.01802 [cs.LG] |
| (orarXiv:2402.01802v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2402.01802 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled An Auction-based Marketplace for Model Trading in Federated Learning, by Yue Cui and 5 other authors
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