Machine Learning Data Markets: Trading Data using a Multi-Agent System

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Baghcheband, H (Autor) Outra; Carlos Soares (Autor) FEUP; reis, lp (Autor) FEUP

Ata de Conferência Internacional

Título: IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2022, Niagara Falls, ON, Canada, November 17-20, 2022

Páginas: 450-457

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00Y-D1F

DOI: 10.1109/wi-iat55865.2022.00073

Abstract (EN): The amount of data produced by distributed devices, such as smart devices and the IoT, is increasing continuously. The cost of transmitting data and also distributed computing power raise interest in distributed data mining (DDM). However, in a pure DDM scenario, data availability may not be enough to generate reliable models in a distributed environment. So, the ability to exchange data efficiently and effectively will become a crucial component of DDM. In this paper, we propose the concept of the Machine Learning Data Market (MLDM), a framework for the exchange of data among autonomous agents. We consider a set of learning agents in a cooperative distributed ML, where agents negotiate data to improve the models they use locally. In the proposed data market, the system's predictive accuracy is investigated, as well as the economic value of data. The question addressed in this paper is: How data exchange among the agents will improve the accuracy of the learning model. Agent budget is defined as a limitation of negotiation. We defined a multi-agent system with negotiation and assessed it against the multi-agent system baseline and the single-agent system. The proposed framework is analyzed based on the different sizes of batch data collected over time to find out how this changes the effect of the negotiation on the accuracy of the model. The results indicate that even simple negotiation among agents increases their learning accuracy.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8