MLDM: Machine Learning Data Market Based on Multi-Agent Systems

Nowadays, autonomous agents, IoT, 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 has no value and, thus, is exchanged for free. Machine Learning Data Market (MLDM), a framework based on Multi-Agent Systems (MASs), where a market-based perspective on data exchange, was recently proposed. In the MLDM, each agent trains its model based on both local data and data bought from other agents. Although 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.