{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T20:14:08Z","timestamp":1783196048341,"version":"3.54.6"},"reference-count":83,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Modern machine learning (ML) methods perform remarkably across a number of diagnostic tasks. Despite this performance, the integration of ML methods in healthcare is relatively limited. While there are a variety of reasons for this, it is notable that most approaches ignore additional constraints that must be made in the healthcare setting. In particular, there may be a relative paucity of data from any single institution; therefore, collaboration is necessary in order to amass a dataset suitable for ML. Furthermore, data may be heterogeneous, with different labels and different input dimensions. Finally, respecting patient privacy is paramount. In this study, we train a classifier under the assumptions of (1) data distributed across multiple institutions, (2) highly heterogeneous data, and (3) a requirement for patient privacy. We enable site-awareness using a global average pooling module to capture high-level information about electrocardiogram (ECG) recording methods combined with a ResNet to encode specific features in ECGs, and we demonstrate that the proposed site-aware ResNet (SA-ResNet) outperforms other state-of-the-art approaches in cardiovascular disease diagnosis. On a highly heterogeneous dataset constructed from three independent datasets distributed unevenly across seven institutions, the proposed model achieves an accuracy, precision, recall, and F1 score of 76.3%, 69.5%, 76.8%, and 73.0%, respectively.<\/jats:p>","DOI":"10.3390\/info17060573","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:33:44Z","timestamp":1781073224000},"page":"573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Site-Aware Federated Learning via Embedding and Resampling with Electrocardiograms"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2598-6837","authenticated-orcid":false,"given":"Wesley","family":"Chorney","sequence":"first","affiliation":[{"name":"College of Medicine and Health, University College Cork, T12 K8AF Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sing Hui","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Limerick, V94 T9PX Limerick, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6827-5751","authenticated-orcid":false,"given":"Haifeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"ref_1","unstructured":"Markit, I. 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