{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T23:54:01Z","timestamp":1780098841381,"version":"3.54.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100023323","name":"Technische Hochschule Ulm","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100023323","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Healthc Inform Res"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Personal health data is subject to privacy regulations, making it challenging to apply centralized data-driven methods in healthcare, where personalized training data is frequently used. Federated Learning (FL) promises to provide a decentralized solution to this problem. In FL, siloed data is used for the model training to ensure data privacy. In this paper, we investigate the viability of the federated approach using the detection of COVID-19 pneumonia as a use case. 1411 individual chest radiographs, sourced from the public data repository COVIDx8 are used. The dataset contains radiographs of 753 normal lung findings and 658 COVID-19 related pneumonias. We partition the data unevenly across five separate data silos in order to reflect a typical FL scenario. For the binary image classification analysis of these radiographs, we propose <jats:italic>ResNetFed<\/jats:italic>, a pre-trained ResNet50 model modified for federation so that it supports <jats:italic>Differential Privacy<\/jats:italic>. In addition, we provide a customized FL strategy for the model training with COVID-19 radiographs. The experimental results show that ResNetFed clearly outperforms locally trained ResNet50 models. Due to the uneven distribution of the data in the silos, we observe that the locally trained ResNet50 models perform significantly worse than ResNetFed models (mean accuracies of 63% and 82.82%, respectively). In particular, ResNetFed shows excellent model performance in underpopulated data silos, achieving up to +34.9 percentage points higher accuracy compared to local ResNet50 models. Thus, with ResNetFed, we provide a federated solution that can assist the initial COVID-19 screening in medical centers in a privacy-preserving manner.<\/jats:p>","DOI":"10.1007\/s41666-023-00132-7","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T12:01:36Z","timestamp":1686744096000},"page":"203-224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs"],"prefix":"10.1007","volume":"7","author":[{"given":"Pascal","family":"Riedel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reinhold","family":"von Schwerin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Schaudt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Hafner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Sp\u00e4te","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"issue":"1","key":"132_CR1","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1038\/s42256-022-00601-5","volume":"5","author":"A Brauneck","year":"2023","unstructured":"Brauneck A, Schmalhorst L, Kazemi Majdabadi MM, Bakhtiari M, V\u00f6lker U, Saak CC, Baumbach J, Baumbach L, Buchholtz G (2023) Federated machine learning in data-protection-compliant research. Nature Machine Intelligence 5(1):2\u20134. https:\/\/doi.org\/10.1038\/s42256-022-00601-5","journal-title":"Nature Machine Intelligence"},{"key":"132_CR2","unstructured":"Murakonda SK, Shokri R (2020) ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning. arXiv . https:\/\/doi.org\/10.48550\/ARXIV.2007.09339"},{"key":"132_CR3","doi-asserted-by":"publisher","unstructured":"Can YS, Ersoy C (2021) Privacy-preserving federated deep learning for wearable iot-based biomedical monitoring. ACM Trans. Internet Technol. 21(1). https:\/\/doi.org\/10.1145\/3428152","DOI":"10.1145\/3428152"},{"key":"132_CR4","doi-asserted-by":"publisher","unstructured":"Larrucea X, Moffie M, Asaf S, Santamaria I (2020) Towards a gdpr compliant way to secure european cross border healthcare industry 4.0.Computer Standards & Interfaces 69:103408. https:\/\/doi.org\/10.1016\/j.csi.2019.103408","DOI":"10.1016\/j.csi.2019.103408"},{"issue":"3","key":"132_CR5","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1001\/jama.2018.5630","volume":"320","author":"IG Cohen","year":"2018","unstructured":"Cohen IG, Mello MM (2018) Hipaa and protecting health information in the 21st century. JAMA 320(3):231\u2013232. https:\/\/doi.org\/10.1001\/jama.2018.5630","journal-title":"JAMA"},{"key":"132_CR6","doi-asserted-by":"crossref","unstructured":"Shao Y (2021) Personal information protection: China\u2019s path choice. US-China L. Rev. 18, 227. https:\/\/doi.org\/10.17265\/1548-6605\/2021.05","DOI":"10.17265\/1548-6605\/2021.05.003"},{"issue":"1","key":"132_CR7","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1093\/jlb\/lsz013","volume":"6","author":"MB Forcier","year":"2019","unstructured":"Forcier MB, Gallois H, Mullan S, Joly Y (2019) Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers? J. Law Biosci. 6(1):317\u2013335","journal-title":"J. Law Biosci."},{"issue":"1","key":"132_CR8","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1111\/rego.12349","volume":"16","author":"R Gellert","year":"2022","unstructured":"Gellert R (2022) Comparing definitions of data and information in data protection law and machine learning: A useful way forward to meaningfully regulate algorithms? Regulation & Governance 16(1):156\u2013176. https:\/\/doi.org\/10.1111\/rego.12349","journal-title":"Regulation & Governance"},{"issue":"3","key":"132_CR9","doi-asserted-by":"publisher","first-page":"241","DOI":"10.13052\/jmbmit2245-456X.434","volume":"4","author":"P Lindgren","year":"2016","unstructured":"Lindgren P (2016) Gdpr regulation impact on different business models and businesses. Journal of Multi Business Model Innovation and Technology 4(3):241\u2013254","journal-title":"Journal of Multi Business Model Innovation and Technology"},{"issue":"8","key":"132_CR10","doi-asserted-by":"publisher","first-page":"5628","DOI":"10.1109\/TII.2022.3144016","volume":"18","author":"X Liu","year":"2022","unstructured":"Liu X, Zhao J, Li J, Cao B, Lv Z (2022) Federated neural architecture search for medical data security. IEEE Transactions on Industrial Informatics 18(8):5628\u20135636. https:\/\/doi.org\/10.1109\/TII.2022.3144016","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"132_CR11","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BAy (2017) Communication-Efficient Learning of Deep Networks from Decentralized Data. In: Singh A, Zhu J (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 54. PMLR, ???, pp. 1273\u20131282. https:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"key":"132_CR12","doi-asserted-by":"publisher","unstructured":"Kone\u010dn\u00fd J, McMahan HB, Ramage D, Richt\u2019arik P (2016) Federated Optimization: Distributed Machine Learning for On-Device Intelligence. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1610.02527","DOI":"10.48550\/ARXIV.1610.02527"},{"issue":"3","key":"132_CR13","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1111\/tmi.13383","volume":"25","author":"TP Velavan","year":"2020","unstructured":"Velavan TP (2020) Meyer CG (2020) The covid-19 epidemic. Tropical medicine & international health\u202f: TM & IH 25(3):278\u2013280. https:\/\/doi.org\/10.1111\/tmi.13383","journal-title":"Tropical medicine & international health : TM & IH"},{"issue":"4","key":"132_CR14","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1007\/s10096-022-04417-4","volume":"41","author":"NN Nguyen","year":"2022","unstructured":"Nguyen NN, Hoang VT, Dao TL, Dudouet P, Eldin C, Gautret P (2022) Clinical patterns of somatic symptoms in patients suffering from post-acute long covid: a systematic review. European Journal of Clinical Microbiology & Infectious Diseases 41(4):515\u2013545. https:\/\/doi.org\/10.1007\/s10096-022-04417-4","journal-title":"European Journal of Clinical Microbiology & Infectious Diseases"},{"issue":"2","key":"132_CR15","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.acra.2022.06.002","volume":"30","author":"F Rizzetto","year":"2023","unstructured":"Rizzetto F, Gnocchi G, Travaglini F, Di Rocco G, Rizzo A, Carbonaro LA, Vanzulli A (2023) Impact of covid-19 pandemic on the workload of diagnostic radiology: A 2-year observational study in a tertiary referral hospital. Academic Radiology 30(2):276\u2013284. https:\/\/doi.org\/10.1016\/j.acra.2022.06.002","journal-title":"Academic Radiology"},{"key":"132_CR16","doi-asserted-by":"publisher","unstructured":"Kerpel A, Apter S, Nissan N, Houri-Levi E, Klug M, Amit S, Konen E, Marom EM (2020) Diagnostic and prognostic value of chest radiographs for covid-19 at presentation.West J Emerg Med 1067\u20131075. https:\/\/doi.org\/10.5811\/westjem.2020.7.48842","DOI":"10.5811\/westjem.2020.7.48842"},{"key":"132_CR17","doi-asserted-by":"publisher","unstructured":"Sharma H, Jain JS, Bansal P, Gupta S (2020) Feature extraction and classification of chest x-ray images using cnn to detect pneumonia. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp 227\u201323. https:\/\/doi.org\/10.1109\/Confluence47617.2020.9057809","DOI":"10.1109\/Confluence47617.2020.9057809"},{"issue":"5","key":"132_CR18","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1007\/s10489-020-02010-w","volume":"51","author":"S Hira","year":"2021","unstructured":"Hira S, Bai A, Hira S (2021) An automatic approach based on cnn architecture to detect covid-19 disease from chest x-ray images. Applied Intelligence 51(5):2864\u20132889. https:\/\/doi.org\/10.1007\/s10489-020-02010-w","journal-title":"Applied Intelligence"},{"issue":"1","key":"132_CR19","doi-asserted-by":"publisher","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports 10(1):19549. https:\/\/doi.org\/10.1038\/s41598-020-76550-z","journal-title":"Scientific Reports"},{"key":"132_CR20","doi-asserted-by":"publisher","first-page":"4466","DOI":"10.1109\/ACCESS.2018.2885997","volume":"7","author":"S Xu","year":"2019","unstructured":"Xu S, Wu H (2019) Bie R (2019) Cxnet-m1: Anomaly detection on chest x-rays with image-based deep learning. IEEE Access 7:4466\u20134477. https:\/\/doi.org\/10.1109\/ACCESS.2018.2885997","journal-title":"IEEE Access"},{"key":"132_CR21","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"MEH Chowdhury","year":"2020","unstructured":"Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, Islam KR, Khan MS, Iqbal A, Emadi NA, Reaz MBI, Islam MT (2020) Can ai help in screening viral and covid-19 pneumonia? IEEE Access 8:132665\u2013132676. https:\/\/doi.org\/10.1109\/ACCESS.2020.3010287","journal-title":"IEEE Access"},{"key":"132_CR22","unstructured":"(2022) Is there any chance that Resnet 50 works better than Resnet 101? https:\/\/bit.ly\/3r61nAc. Accessed 18 Sept 2022"},{"key":"132_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWAMTIP.2017.8301487","author":"R Khan","year":"2018","unstructured":"Khan R, Zhang X, Kumar R, Tariq A, Kumar R (2018). Analysis of resnet model for malicious code detection. https:\/\/doi.org\/10.1109\/ICCWAMTIP.2017.8301487","journal-title":"Analysis of resnet model for malicious code detection."},{"key":"132_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107330","volume":"106","author":"I Feki","year":"2021","unstructured":"Feki I, Ammar S, Kessentini Y, Muhammad K (2021) Federated learning for covid-19 screening from chest x-ray images. Applied Soft Computing 106:107330. https:\/\/doi.org\/10.1016\/j.asoc.2021.107330","journal-title":"Applied Soft Computing"},{"key":"132_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110749","volume":"145","author":"S Rajpal","year":"2021","unstructured":"Rajpal S, Lakhyani N, Singh AK, Kohli R, Kumar N (2021) Using handpicked features in conjunction with resnet-50 for improved detection of covid-19 from chest x-ray images. Chaos, Solitons & Fractals 145:110749. https:\/\/doi.org\/10.1016\/j.chaos.2021.110749","journal-title":"Chaos, Solitons & Fractals"},{"key":"132_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114054","volume":"164","author":"AM Ismael","year":"2021","unstructured":"Ismael AM, \u015eeng\u00fcr A (2021) Deep learning approaches for covid-19 detection based on chest x-ray images. Expert Systems with Applications 164:114054. https:\/\/doi.org\/10.1016\/j.eswa.2020.114054","journal-title":"Expert Systems with Applications"},{"key":"132_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103848","volume":"78","author":"G Srivastava","year":"2022","unstructured":"Srivastava G, Chauhan A, Jangid M, Chaurasia S (2022) Covixnet: A novel and efficient deep learning model for detection of covid-19 using chest xray images. Biomedical Signal Processing and Control 78:103848. https:\/\/doi.org\/10.1016\/j.bspc.2022.103848","journal-title":"Biomedical Signal Processing and Control"},{"key":"132_CR28","doi-asserted-by":"publisher","first-page":"105979","DOI":"10.1016\/j.compbiomed.2022.105979","volume":"149","author":"G Srivastava","year":"2022","unstructured":"Srivastava G, Pradhan N, Saini Y (2022) Ensemble of deep neural networks based on condorcet\u2019s jury theorem for screening covid-19 and pneumonia from radiograph images. Computers in Biology and Medicine 149:105979. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105979","journal-title":"Computers in Biology and Medicine"},{"key":"132_CR29","doi-asserted-by":"publisher","first-page":"109872","DOI":"10.1016\/j.asoc.2022.109872","volume":"132","author":"G Srivastava","year":"2023","unstructured":"Srivastava G, Chauhan A, Pradhan N (2023) Cjt-deo: Condorcet\u2019s jury theorem and differential evolution optimization based ensemble of deep neural networks for pulmonary and colorectal cancer classification. Applied Soft Computing 132:109872. https:\/\/doi.org\/10.1016\/j.asoc.2022.109872","journal-title":"Applied Soft Computing"},{"key":"132_CR30","doi-asserted-by":"publisher","unstructured":"Srivastava G, Chauhan A, Jangid M, Jain A (2022) An analysis of deep learning models to diagnose covid-19 using radiography images. In: 2022 International Conference for Advancement in Technology (ICONAT), pp 1\u20137. https:\/\/doi.org\/10.1109\/ICONAT53423.2022.9725858","DOI":"10.1109\/ICONAT53423.2022.9725858"},{"key":"132_CR31","doi-asserted-by":"publisher","first-page":"107160","DOI":"10.1016\/j.asoc.2021.107160","volume":"103","author":"F Demir","year":"2021","unstructured":"Demir F (2021) Deepcoronet: A deep lstm approach for automated detection of covid-19 cases from chest x-ray images. Applied soft computing 103:107160\u2013107160. https:\/\/doi.org\/10.1016\/j.asoc.2021.107160","journal-title":"Applied soft computing"},{"key":"132_CR32","doi-asserted-by":"publisher","unstructured":"Yee SLK, Raymond WJK (2020) Pneumonia diagnosis using chest x-ray images and machine learning. In: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology. ICBET 2020, Association for Computing Machinery, New York, NY, USA, pp 101\u2013105. https:\/\/doi.org\/10.1145\/3397391.3397412","DOI":"10.1145\/3397391.3397412"},{"key":"132_CR33","doi-asserted-by":"publisher","unstructured":"Addo D, Zhou S, Jackson JK, Nneji GU, Monday HN, Sarpong K, Patamia RA, Ekong F, Owusu-Agyei CA (2022) Evae-net: An ensemble variational autoencoder deep learning network for covid-19 classification based on chest x-ray images. Diagnostics 12(11). https:\/\/doi.org\/10.3390\/diagnostics12112569","DOI":"10.3390\/diagnostics12112569"},{"key":"132_CR34","unstructured":"Danezis G, Domingo-Ferrer J, Hansen M, Hoepman J-H, Metayer DL, Tirtea R, Schiffner S (2015) Privacy and data protection by design-from policy to engineering. arXiv preprint. arXiv:1501.03726"},{"key":"132_CR35","doi-asserted-by":"crossref","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R (2021) Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14(1-2):1\u2013210","DOI":"10.1561\/2200000083"},{"key":"132_CR36","doi-asserted-by":"publisher","unstructured":"Ruby U, Yendapalli V (2020) Binary cross entropy with deep learning technique for image classification. International Journal of Advanced Trends in Computer Science and Engineering 9. https:\/\/doi.org\/10.30534\/ijatcse\/2020\/175942020","DOI":"10.30534\/ijatcse\/2020\/175942020"},{"key":"132_CR37","unstructured":"Wang L (2021) COVIDx Dataset. http:\/\/bit.ly\/3I2Rz1X. Accessed 18 Sept 2022"},{"key":"132_CR38","doi-asserted-by":"publisher","first-page":"e0242958","DOI":"10.1371\/journal.pone.0242958","volume":"15","author":"I Arevalo-Rodriguez","year":"2020","unstructured":"Arevalo-Rodriguez I, Buitrago-Garcia D, Simancas-Racines D, Zambrano P, Campo R, Ciapponi A, Sued O, Garc\u00eda L, Rutjes A, Low N, Bossuyt P, Perez-Molina J, Zamora J (2020) False-negative results of initial rt-pcr assays for covid-19: A systematic review. PLoS ONE 15:e0242958. https:\/\/doi.org\/10.1371\/journal.pone.0242958","journal-title":"PLoS ONE"},{"issue":"3","key":"132_CR39","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1148\/radiol.2021204522","volume":"299","author":"JP Kanne","year":"2021","unstructured":"Kanne JP, Bai H, Bernheim A, Chung M, Haramati LB, Kallmes DF, Little BP, Rubin G, Sverzellati N (2021) Covid-19 imaging: What we know now and what remains unknown. Radiology 299(3):262\u2013279. https:\/\/doi.org\/10.1148\/radiol.2021204522","journal-title":"Radiology"},{"issue":"01","key":"132_CR40","doi-asserted-by":"publisher","first-page":"9808","DOI":"10.1609\/aaai.v33i01.33019808","volume":"33","author":"P Flach","year":"2019","unstructured":"Flach P (2019) Performance evaluation in machine learning: The good, the bad, the ugly, and the way forward. Proceedings of the AAAI Conference on Artificial Intelligence 33(01):9808\u20139814. https:\/\/doi.org\/10.1609\/aaai.v33i01.33019808","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"132_CR41","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet:A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"132_CR42","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"132_CR43","doi-asserted-by":"publisher","unstructured":"Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. CCS\u201916, . Association for Computing Machinery, New York, NY, USA . pp 308-318. https:\/\/doi.org\/10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"key":"132_CR44","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQS, Poor HV (2020) Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security 15:3454\u20133469. https:\/\/doi.org\/10.1109\/TIFS.2020.2988575","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"132_CR45","doi-asserted-by":"publisher","unstructured":"Asad M, Moustafa A, Ito T (2020) Fedopt: Towards communication efficiency and privacy preservation in federated learning. Applied Sciences 10(8). https:\/\/doi.org\/10.3390\/app10082864","DOI":"10.3390\/app10082864"},{"key":"132_CR46","doi-asserted-by":"publisher","unstructured":"Du J, Li S, Chen X, Chen S, Hong M (2021) Dynamic Differential-Privacy Preserving SGD. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2111.00173","DOI":"10.48550\/ARXIV.2111.00173"},{"key":"132_CR47","unstructured":"(2022) Integrate.ai: Developer tools for privacy-safe federated learning and analytics. https:\/\/www.integrate.ai\/. Accessed 18 Sept 2022"},{"key":"132_CR48","unstructured":"(2020) Why BatchNorm layer is not compatible with DP-SGD. https:\/\/discuss.pytorch.org\/t\/why-batchnorm-layer-is-not-compatible-with-dp-sgd\/154208. Accessed 18 Sept 2022"},{"key":"132_CR49","unstructured":"(2022) BatchNorm2D - PyTorch 1.12 Documentation. https:\/\/bit.ly\/3LuSGJe. Accessed 18 Sept 2022"},{"key":"132_CR50","unstructured":"(2022) Identity - PyTorch 1.12 Documentation. https:\/\/bit.ly\/3UlOIGE. Accessed 18 Sept 2022"},{"key":"132_CR51","unstructured":"(2022) Clip Grad Norm - PyTorch 1.12 Documentation.https:\/\/bit.ly\/3QTKFP4. Accessed 18 Sept 2022"},{"key":"132_CR52","doi-asserted-by":"publisher","unstructured":"Kone\u010dn\u00fd J, McMahan HB, Yu FX, Richtarik P, Suresh AT, Bacon D (2016) Federated learning: Strategies for improving communication efficiency. In: NIPS Workshop on Private Multi-Party Machine Learning. https:\/\/doi.org\/10.48550\/ARXIV.1610.05492","DOI":"10.48550\/ARXIV.1610.05492"},{"key":"132_CR53","unstructured":"Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Kone\u010dn\u00fd J, Mazzocchi S, McMahan B, Van Overveldt T, Petrou D, Ramage D, Roselander J (2019) Towards federated learning at scale: System design. In: Talwalkar A, Smith V, Zaharia M.(eds.) Proceedings of Machine Learning and Systems, vol. 1, pp 374\u2013388. https:\/\/bit.ly\/3YFGNWE"},{"key":"132_CR54","doi-asserted-by":"publisher","unstructured":"Yoo JH, Son HM, Jeong H, Jang E-H, Kim AY, Yu HY, Jeon HJ, Chung T-M (2021) Personalized federated learning with clustering: Non-iid heart rate variability data application. In: 2021 International Conference on Information and Communication Technology Convergence (ICTC), pp 1046\u20131051. https:\/\/doi.org\/10.1109\/ICTC52510.2021.9620852","DOI":"10.1109\/ICTC52510.2021.9620852"},{"key":"132_CR55","doi-asserted-by":"publisher","unstructured":"Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. https:\/\/doi.org\/10.48550\/ARXIV.1806.00582","DOI":"10.48550\/ARXIV.1806.00582"},{"key":"132_CR56","unstructured":"Li X, Huang K, Yang W, Wang S, Zhang Z (2019) On the convergence of fedavg on non-iid data. arXiv preprint. arXiv:1907.02189"},{"key":"132_CR57","doi-asserted-by":"publisher","unstructured":"Li Q, Diao Y, Chen Q, He B (2022) Federated learning on non-iid data silos: An experimental study. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp 965\u2013978. https:\/\/doi.org\/10.1109\/ICDE53745.2022.00077","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"132_CR58","doi-asserted-by":"publisher","unstructured":"Kim G, Kim J, Han B (2022) Communication-Efficient Federated Learning with Acceleration of Global Momentum. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2201.03172","DOI":"10.48550\/ARXIV.2201.03172"},{"key":"132_CR59","unstructured":"(2022) Autism Sharing Initiative. https:\/\/www.autismsharinginitiative.org\/ Accessed 18 Sept 2022"},{"key":"132_CR60","unstructured":"Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) SCAFFOLD: Stochastic controlled averaging for federated learning. In: III HD, Singh A (eds.) Proceedings of the 37th International Conference on Machine Learning (PMLR), vol. 119, pp 5132\u20135143. https:\/\/proceedings.mlr.press\/v119\/karimireddy20a.html"}],"container-title":["Journal of Healthcare Informatics Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-023-00132-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41666-023-00132-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-023-00132-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T12:02:45Z","timestamp":1687694565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41666-023-00132-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6]]},"references-count":60,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["132"],"URL":"https:\/\/doi.org\/10.1007\/s41666-023-00132-7","relation":{},"ISSN":["2509-4971","2509-498X"],"issn-type":[{"value":"2509-4971","type":"print"},{"value":"2509-498X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6]]},"assertion":[{"value":"30 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article and there are no financial interests","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"The data and models used are purely for scientific purposes and do not replace a clinical COVID-19 diagnosis by medical specialists","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"Not applicable","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}