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Machine Learning Data Market Based on Multiagent Systems
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AI
DOI：10.1109/MIC.2024.3399049.png)
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Abstract
En 中文
Today, autonomous agents, the Internet of Things, and smart devices produce more and more distributed data and use them to learn models for different purposes. One challenge is that learning from local data only may lead to suboptimal models. Thus, better models are expected if agents can exchange data, leading to approaches such as federated learning. However, these approaches assume that data have no value and, thus, is exchanged for free. A machine learning data market (MLDM), a framework based on multiagent systems with a market-based perspective on data exchange, was recently proposed. In an MLDM, each agent trains its model based on both local data and data bought from other agents. Although the empirical results are interesting, several challenges are still open, including data acquisition and data valuation. The MLDM is an illustrative example of how the value of data can and should be integrated into the design of distributed ML systems.
Keywords:
Data models
Distributed databases
Cost accounting
Biological system modeling
Training
Machine learning
Computational modeling
Autonomous agents
Data analysis
Market research
Internet of Things
Multi-agent systems
Smart devices
Information exchange
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