{"title":"一个分散的匹配理论框架来匹配数据和算法提供者","authors":"Chaouki Ben Issaid;Mehdi Bennis","doi":"10.1109/LNET.2025.3560459","DOIUrl":null,"url":null,"abstract":"This letter 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.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"140-144"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964373","citationCount":"0","resultStr":"{\"title\":\"A Decentralized Matching Theory Framework to Match Data and Algorithms Providers\",\"authors\":\"Chaouki Ben Issaid;Mehdi Bennis\",\"doi\":\"10.1109/LNET.2025.3560459\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This letter 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.\",\"PeriodicalId\":100628,\"journal\":{\"name\":\"IEEE Networking Letters\",\"volume\":\"7 2\",\"pages\":\"140-144\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964373\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Networking Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10964373/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Networking Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10964373/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Decentralized Matching Theory Framework to Match Data and Algorithms Providers
This letter 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.
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