{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:32:49Z","timestamp":1778689969942,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program for High-Quality Research Achievement of Inner Mongolia University of Finance and Economics","award":["GZCG24208"],"award-info":[{"award-number":["GZCG24208"]}]},{"name":"Program for High-Quality Research Achievement of Inner Mongolia University of Finance and Economics","award":["NCXKY25050"],"award-info":[{"award-number":["NCXKY25050"]}]},{"DOI":"10.13039\/501100004763","name":"Natural Science Foundation of Inner Mongolia","doi-asserted-by":"crossref","award":["2024LHMS01010"],"award-info":[{"award-number":["2024LHMS01010"]}],"id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Non-IID is one of the key challenges in federated learning. Data heterogeneity may lead to slower convergence, reduced accuracy, and more training rounds. To address the common Non-IID data distribution problem in federated learning, we propose a comprehensive dynamic optimization approach based on existing methods. It leverages MAP estimation of the Dirichlet parameter \u03b2 to dynamically adjust the regularization coefficient \u03bc and introduces orthogonal gradient coefficients \u0394i to mitigate gradient interference among different classes. The approach is compatible with existing federated learning frameworks and can be easily integrated. Achieves significant accuracy improvements in both mildly and severely Non-IID scenarios while maintaining a strong performance lower bound.<\/jats:p>","DOI":"10.3390\/info16100861","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T15:05:06Z","timestamp":1759763106000},"page":"861","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Addressing Non-IID with Data Quantity Skew in Federated Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2617-6894","authenticated-orcid":false,"given":"Narisu","family":"Cha","sequence":"first","affiliation":[{"name":"The School of Computer and Information Management, Inner Mongolia University of Finance and Economics, Huhort 010051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6851-8295","authenticated-orcid":false,"given":"Long","family":"Chang","sequence":"additional","affiliation":[{"name":"The School of Statistics and Mathematics, Inner Mongolia University of Finance and Economics, Huhort 010051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"ref_1","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A.y. 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