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In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support (such as biomedical image diagnostics), is computed at the client level. We leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device\u2019s computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenarios, our architecture demonstrates unparalleled performance and robustness in terms of both accuracy and efficiency.<\/jats:p>","DOI":"10.1007\/s11432-024-4162-2","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T04:55:11Z","timestamp":1743742511000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["On-device diagnostic recommendation with heterogeneous federated BlockNets"],"prefix":"10.1007","volume":"68","author":[{"given":"Minh Hieu","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thanh Trung","family":"Huynh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thanh Toan","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phi Le","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hien Thu","family":"Pham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Jo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thanh Tam","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"4162_CR1","doi-asserted-by":"publisher","first-page":"130301","DOI":"10.1007\/s11432-022-3652-2","volume":"66","author":"G X Zhu","year":"2023","unstructured":"Zhu G X, Lyu Z H, Jiao X, et al. 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