{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:54:45Z","timestamp":1774720485914,"version":"3.50.1"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"5s","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["CNS-2132385"],"award-info":[{"award-number":["CNS-2132385"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, making them vulnerable to degradation in performance with dynamic changes across indoor environments. To address the challenges due to device heterogeneity and lack of adaptivity, we propose a novel embedded ML framework called\n            <jats:italic>FedHIL<\/jats:italic>\n            . Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments while also preserving user data privacy.\n            <jats:italic>FedHIL<\/jats:italic>\n            integrates a domain-specific selective weight adjustment approach to preserve the ML model's performance for indoor localization during FL, even in the presence of extremely noisy data. Experimental evaluations in diverse real-world indoor environments and with heterogeneous mobile devices show that\n            <jats:italic>FedHIL<\/jats:italic>\n            outperforms state-of-the-art FL and non-FL indoor localization frameworks.\n            <jats:italic>FedHIL<\/jats:italic>\n            is able to achieve 1.62 \u00d7 better localization accuracy on average than the best performing FL-based indoor localization framework from prior work.\n          <\/jats:p>","DOI":"10.1145\/3607919","type":"journal-article","created":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T13:33:18Z","timestamp":1694266398000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1004-3798","authenticated-orcid":false,"given":"Danish","family":"Gufran","sequence":"first","affiliation":[{"name":"Colorado State University, Fort Collins, CO, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0846-0066","authenticated-orcid":false,"given":"Sudeep","family":"Pasricha","sequence":"additional","affiliation":[{"name":"Colorado State University, Fort Collins, CO, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Indoor localization with smartphones","author":"Langlois C.","year":"2017","unstructured":"C. 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