Machine Learning Data Markets: Evaluating the Impact of Data Exchange on the Agent Learning Performance

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Baghcheband, H (Author) Other; Carlos Soares (Author) FEUP; reis, lp (Author) FEUP

Conference proceedings International

Title: PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT I

Pages: 337-348

22nd EPIA Conference on Artificial Intelligence (EPIA)

Azores, PORTUGAL, SEP 05-08, 2023

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Other information

Authenticus ID: P-00Z-M0C

DOI: 10.1007/978-3-031-49008-8_27

Abstract (EN): In recent years, the increasing availability of distributed data has led to a growing interest in transfer learning across multiple nodes. However, local data may not be adequate to learn sufficiently accurate models, and the problem of learning from multiple distributed sources remains a challenge. To address this issue, Machine Learning Data Markets (MLDM) have been proposed as a potential solution. In MLDM, autonomous agents exchange relevant data in a cooperative relationship to improve their models. Previous research has shown that data exchange can lead to better models, but this has only been demonstrated with only two agents. In this paper, we present an extended evaluation of a simple version of the MLDM framework in a collaborative scenario. Our experiments show that data exchange has the potential to improve learning performance, even in a simple version of MLDM. The findings conclude that there exists a direct correlation between the number of agents and the gained performance, while an inverse correlation was observed between the performance and the data batch sizes. The results of this study provide important insights into the effectiveness of MLDM and how it can be used to improve learning performance in distributed systems. By increasing the number of agents, a more efficient system can be achieved, while larger data batch sizes can decrease the global performance of the system. These observations highlight the importance of considering both the number of agents and the data batch sizes when designing distributed learning systems using the MLDM framework.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12