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Thus, FL faces the challenge of data distribution and heterogeneity, where non-Independent and Identically Distributed (non-IID) data across edge devices may yield in significant accuracy drop. Furthermore, the limited computation and communication capabilities of edge devices increase the likelihood of stragglers, thus leading to slow model convergence. In this article, we propose the FedDHAD FL framework, which comes with two novel methods: dynamic heterogeneous model aggregation (FedDH) and adaptive dropout (FedAD). FedDH dynamically adjusts the weights of each local model within the model aggregation process based on the non-IID degree of heterogeneous data to deal with the statistical data heterogeneity. FedAD performs neuron-adaptive operations in response to heterogeneous devices to improve accuracy while achieving superb efficiency. The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and computation cost (up to 15.0% smaller).<\/jats:p>","DOI":"10.1145\/3749376","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T22:39:57Z","timestamp":1753223997000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4710-5697","authenticated-orcid":false,"given":"Ji","family":"Liu","sequence":"first","affiliation":[{"name":"Baidu Research, Beijing, China and HiThink Research, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9181-3143","authenticated-orcid":false,"given":"Beichen","family":"Ma","sequence":"additional","affiliation":[{"name":"Baidu Research, Beijing, China and Cornell University Cornell Tech, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1979-1645","authenticated-orcid":false,"given":"Qiaolin","family":"Yu","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1895-4243","authenticated-orcid":false,"given":"Ruoming","family":"Jin","sequence":"additional","affiliation":[{"name":"Kent State University, Kent, Ohio, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2677-7021","authenticated-orcid":false,"given":"Jingbo","family":"Zhou","sequence":"additional","affiliation":[{"name":"Baidu Research, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7839-4933","authenticated-orcid":false,"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Auburn University, Auburn, Alabama, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0078-4891","authenticated-orcid":false,"given":"Huaiyu","family":"Dai","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, North Carolina, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1378-4241","authenticated-orcid":false,"given":"Haixun","family":"Wang","sequence":"additional","affiliation":[{"name":"Instacart, San Francisco, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2949-6874","authenticated-orcid":false,"given":"Dejing","family":"Dou","sequence":"additional","affiliation":[{"name":"BEDI Cloud, Beijing, China and Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6506-7538","authenticated-orcid":false,"given":"Patrick","family":"Valduriez","sequence":"additional","affiliation":[{"name":"Inria, University of Montpellier, CNRS, LIRMM, Montpellier, France and LNCC, Petr\u00f3polis, Rio de Janeiro, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"e_1_3_1_3_2","unstructured":"Chunlu Chen Ji Liu Haowen Tan Xingjian Li Kevin I-Kai Wang Peng Li Kouichi Sakurai and Dejing Dou. 2024. 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