A Decentralized Matching Theory Framework to Match Data and Algorithms Providers

This paper presents a novel decentralized matching algorithm (DEMA) for pairing data and algorithm providers in AI ecosystems. DEMA addresses scalability, stability, and matching utility challenges in large-scale environments. Formulated as a two-sided matching game, our decentralized solution enables autonomous decision-making based on local information. Simulations demonstrate DEMA’s near-optimal matching quality and almost perfect stability. Furthermore, DEMA exhibits excellent scalability with execution times and memory usage growing much more slowly than centralized matching as the number of providers increases.