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Federated learning, a decentralized learning framework, was proposed to construct a shared prediction model while keeping owners' data on their own devices. This paper presents an introduction to the emerging federated learning standard and discusses its various aspects, including i) an overview of federated learning, ii) types of federated learning, iii) major concerns and the performance evaluation criteria of federated learning, and iv) associated regulatory requirements. The purpose of this paper is to provide an understanding of the standard and facilitate its usage in model building across organizations while meeting privacy and security concerns.<\/jats:p>","DOI":"10.1145\/3511285.3511291","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T23:15:51Z","timestamp":1641942951000},"page":"18-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["An Introduction to the Federated Learning Standard"],"prefix":"10.1145","volume":"25","author":[{"given":"Ticao","family":"Zhang","sequence":"first","affiliation":[{"name":"Auburn University, Auburn, AL, USA"}]},{"given":"Shiwen","family":"Mao","sequence":"additional","affiliation":[{"name":"Auburn University, Auburn, AL, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Artificial Intelligence: A Modern Approach","author":"Russell S.","year":"2002","unstructured":"S. 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