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Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification
Open accessAbstract
In response to increasing data privacy regulations, this work examines the use of federated learning for deep residual networks to diagnose cardiac abnormalities from electrocardiogram (ECG) data. This approach allows medical institutions to collaborate without exchanging raw patient data. We utilize the publicly available data from the PhysioNet/Computing in Cardiology Challenge 2021, featuring diverse ECG databases, to compare the classification performance of three federated learning methods against both central training with data sharing and isolated training scenarios. We show that federated learning outperforms ECG classifiers trained in isolation. In particular, our findings demonstrate that a globally trained model fine-tuned to specific local datasets surpasses non-collaborative approaches. This shows that models trained in federation learn general features that can be tailored to specific tasks. Furthermore, federated learning almost matches the performance of central training with data sharing on out-of-distribution data from non-participating institutions. These results highlight the ability of federated learning in developing models that generalize well across diverse patient data, without the need to share data among institutions, thus addressing data privacy concerns.
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ECG-specific studies have demonstrated the feasibility of federated training across heterogeneous datasets, including deep neural networks applied to raw 12-lead ECG signals.
“ECG-specific studies have demonstrated the feasibility of federated training across heterogeneous datasets, including deep neural networks applied to raw 12-lead ECG signals [68].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [68]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
(2014) · Cited by 33,419
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Layer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,762
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
(2010) · Cited by 8,261
Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11787006_1 · Cited by 6,125
SGDR: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309
The impact of the MIT-BIH Arrhythmia Database
Moody, G. B., & Mark, R. G. (2001) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.932724 · Cited by 4,834
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015 · cited 1×
“In the domain of electrocardiography (ECG), institutions tend to create ever larger data silos [7], [8], [9], [10], which often include institutional biases related to race, gender, age, demographic features, socioeconomic status, or medical equipment [11], [12], [13].”
BackgroundFederated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
Wu, Y., & He, K. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01198-w · Cited by 3,601
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2019) · arXiv · doi:10.48550/arxiv.1910.06378 · Cited by 1,734
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · cited 1×
“In the domain of electrocardiography (ECG), institutions tend to create ever larger data silos [7], [8], [9], [10], which often include institutional biases related to race, gender, age, demographic features, socioeconomic status, or medical equipment [11], [12], [13].”
BackgroundSheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646
On the Convergence of FedAvg on Non-IID Data
Li, X., Huang, K., Yang, W., Wang, S., & Zhang, Z. (2019) · arXiv · doi:10.48550/arxiv.1907.02189 · Cited by 1,575 · cited 1×
“Federated averaging often suffers from client drift when data is heterogeneous (non-IID), which leads clients away from the globally optimal model and slows down the convergence [45], [46].”
BackgroundPTB-XL, a large publicly available electrocardiography dataset
Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., & Schaeffter, T. (2020) · Scientific Data · doi:10.1038/s41597-020-0495-6 · Cited by 1,357
Automatic diagnosis of the 12-lead ECG using a deep neural network
Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P. S., Andersson, C. R., Macfarlane, P. W., Meira, W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15432-4 · Cited by 894
Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01179 · Cited by 891
Inverting Gradients -- How easy is it to break privacy in federated learning?Preprint
Geiping, J., Bauermeister, H., Dröge, H., & Moeller, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14053 · Cited by 848
Adaptive Federated Optimization
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2020) · arXiv · doi:10.48550/arxiv.2003.00295 · Cited by 840
Automatic diagnosis of the 12-lead ECG using a deep neural network
Ribeiro, A. H., Ribeiro, M. H., Paixao, G. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P. S., Andersson, C. R., Macfarlane, P. W., Meira, W., et al. (2020) · RePEc: Research Papers in Economics · doi:10.5281/zenodo.3625006 · Cited by 722
Personalized Federated Learning with Moreau EnvelopesPreprint
Dinh, C. T., Tran, N. H. K., & Nguyen, T. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08848 · Cited by 697
Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., & Hossain, M. S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3011726 · Cited by 620
Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609
Privacy-Preserving Federated Brain Tumour Segmentation
Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578
Regulation (EU) 2015/848 of the European Parliament and of the Council
de Farmacoterapia, R. P. (2016) · Hart Publishing eBooks · doi:10.5040/9781782258674.0010 · Cited by 538
See through Gradients: Image Batch Recovery via GradInversion
Yin, H., Mallya, A., Vahdat, A., Álvarez, J. M., Kautz, J., & Molchanov, P. A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01607 · Cited by 519
Federated Learning and Differential Privacy for Medical Image Analysis
Adnan, M., Kalra, S., Cresswell, J. C., Taylor, G. W., & Tizhoosh, H. (2021) · doi:10.21203/rs.3.rs-1005694/v1 · Cited by 464
The Non-IID Data Quagmire of Decentralized Machine LearningPreprint
Hsieh, K., Phanishayee, A., Mutlu, O., & Gibbons, P. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00189 · Cited by 353
Raghunath, S., Pfeifer, J. M., Ulloa-Cerna, A. E., Nemani, A., Carbonati, T., Jing, L., vanMaanen, D. P., Hartzel, D. N., Ruhl, J. A., Lagerman, B. F., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.047829 · Cited by 312
Health Insurance Portability and Accountability Act (HIPAA)
Shoaf, H. R. (2003) · Plastic and Aesthetic Nursing · doi:10.1097/00006527-200323020-00009 · Cited by 283
Adaptive Methods for Nonconvex Optimization
Zaheer, M., Reddi, S. J., Sachan, D. S., Kale, S., & Kumar, S. (2018) · neural information processing systems · Cited by 267
Federated learning improves site performance in multicenter deep learning without data sharing
Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa341 · Cited by 242 · cited 1×
“Federated learning has found a variety of applications in medical research: brain tumor segmentation [20], [21], breast cancer classification [22], whole prostate segmentation [23], lung cancer detection [24] and ECG interpretation [25], [26], [27], [28], [29], [30], [31].”
MethodEnd-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
Gao, Y., Kim, M., Abuadbba, S., Kim, Y., Thapa, C., Kim, K., Camtep, S. A., Kim, H., & Nepal, S. (2020) · 2020 International Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds51746.2020.00017 · Cited by 240
Designing ECG monitoring healthcare system with federated transfer learning and explainable AI
Raza, A., Tran, K. P., Koehl, L., & Li, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107763 · Cited by 205
Federated Learning for Breast Density Classification: A Real-World Implementation
Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60548-3_18 · Cited by 202
Optimal Multi-Stage Arrhythmia Classification Approach
Zheng, J., Chu, H., Struppa, D. C., Zhang, J., Yacoub, M. H., El-Askary, H. M., Chang, A. C., Ehwerhemuepha, L., Abudayyeh, I., Barrett, A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-59821-7 · Cited by 201
Reyna, M. A., Sadr, N., Alday, E. A. P., Gu, A., Shah, A. J., Robichaux, C. J., Rad, A. B., Elola, A., Seyedi, S. S., Ansari, S., et al. (2021) · Computing in cardiology · doi:10.23919/cinc53138.2021.9662687 · Cited by 200
Federated Learning on Clinical Benchmark Data: Performance Assessment
Lee, G. H., & Shin, S.-Y. (2020) · Journal of Medical Internet Research · doi:10.2196/20891 · Cited by 143
Personalized Real-Time Federated Learning for Epileptic Seizure Detection
Baghersalimi, S., Teijeiro, T., Atienza, D., & Aminifar, A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3096127 · Cited by 123
Scaling Speech Technology to 1, 000+ Languages
Pratap, V., Tjandra, A., Shi, B., Tomasello, P., Babu, A., Kundu, S., Elkahky, A., Ni, Z., Vyas, A., Fazel-Zarandi, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.13516 · Cited by 116
Fishing for User Data in Large-Batch Federated Learning via Gradient MagnificationPreprint
Wen, Y., Geiping, J., Fowl, L., Goldblum, M., & Goldstein, T. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.00580 · Cited by 68
Gustafsson, S., Gedon, D., Lampa, E., Ribeiro, A. H., Holzmann, M. J., Schön, T. B., & Sundström, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-24254-x · Cited by 65
Classification of ECG Using Ensemble of Residual CNNs with Attention Mechanism
Nejedlý, P., Ivora, A., Smíšek, R., Viščor, I., Koscova, Z., Jurák, P., & Plešinger, F. (2021) · Computing in cardiology · doi:10.23919/cinc53138.2021.9662723 · Cited by 55
Issues in the automated classification of multilead ecgs using heterogeneous labels and populations
Reyna, M. A., Sadr, N., Alday, E. A. P., Gu, A., Shah, A. J., Robichaux, C. J., Rad, A. B., Elola, A., Seyedi, S. S., Ansari, S., et al. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac79fd · Cited by 52
Federated Learning for Arrhythmia Detection of Non-IID ECG
Zhang, M., Wang, Y., & Luo, T. (2020) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc51575.2020.9344971 · Cited by 37
Application of Federated Learning Techniques for Arrhythmia Classification Using 12-Lead ECG Signals
Jimenez-Gutierrez, D. M., Hassan, H. M., Landi, L., Vitaletti, A., & Chatzigiannakis, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49361-4_3 · Cited by 37
On Privacy and Personalization in Cross-Silo Federated LearningPreprint
Liu, Z., Hu, S., Wu, Z. S., & Smith, V. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.07902 · Cited by 32
Towards High Generalization Performance on Electrocardiogram Classification
Han, H., Park, S., Min, S., Choi, H.-S., Kim, E., Kim, H., Park, S., Kim, J., Park, J., An, J., et al. (2021) · Computing in cardiology · doi:10.23919/cinc53138.2021.9662737 · Cited by 24
Wickramasinghe, N. L., & Athif, M. (2021) · Computing in cardiology · doi:10.23919/cinc53138.2021.9662787 · Cited by 18
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- 10.5555/3045118.3045167 · DOI
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Zhang, C., Huang, Z., Zhou, C., Qie, A., & Wang, X. A. (2025) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2025.3530790 · Cited by 15
Kapsecker, M., & Jonas, S. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000793 · Cited by 12
Lv, X., Tao, Y., Zhang, Y., & Xue, Y. (2025) · Applied Sciences · doi:10.3390/app15073831 · Cited by 14
Securing deep learning models with differential privacy for cardiovascular disease prediction
Orabe, Z., Vasankari, A., Pahikkala, T., Kaisti, M., & Airola, A. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108502 · Cited by 8
Huang, Z., Yu, L., Contreras, L. F. H., Dwivedi, G., & Kavehei, O. (2025) · Royal Society Open Science · doi:10.1098/rsos.251213 · Cited by 5
Yuksel, B. B., & Metin, A. Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109557 · Cited by 4
Sun, Y., Parameshachari, B. D., & Wang, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3583340 · Cited by 5
Varzideh, F., Pande, S., Jankauskas, S. S., Mone, P., Kansakar, U., & Santulli, G. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1788653 · Cited by 4
A Federated Learning Approach for Predicting Heart Disease Risk
Jia, J., Liu, S., & Wang, F. (2025) · International Conference on Computing, Networks and Internet of Things (CNIOT) · doi:10.1109/cniot65435.2025.11071256 · Cited by 1
Cao, T., Ng, W. S., Shen, C., Li, H., Tan, R., Wang, D., & Huang, H. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3617826 · Cited by 1
He, J., He, J., Xiong, J., He, S., Chen, Y., & Li, X. (2026) · iScience · doi:10.1016/j.isci.2026.115729 · Cited by 1
Rezaei, Z., Amini, M. A., & Banad, Y. M. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111859 · Cited by 1
Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease
Shaulian, S., Zeltser, R., & Makaryus, A. N. (2026) · Diagnostics · doi:10.3390/diagnostics16172834
Securing Deep Learning Models with Differential Privacy for Cardiovascular Disease PredictionPreprint
Orabe, Z., Vasankari, A., Pahikkala, T., Kaisti, M., & Airola, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5375201
Molaei, S., Thakur, A., Clifton, L., Soltan, A. A. S., Schwab, P., Belgrave, D. C. M., Branson, K., & Clifton, D. A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3611069 · Cited by 2
FedMCF-xLSTM: Federated contrastive xLSTM for multimodal multi-label ECG classification
Huan, E., Dun, H., & Li, J. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109612 · Cited by 2
Yuan, S., Wang, Z., Qin, Y., Wang, X. F., Yang, W., & Liu, H. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114317
Cao, T., Shu, Z., Ng, W. S., Zou, T., Li, H., Zhao, H., Shen, C., Chai, P. R., & Huang, H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3726810
Hcini, G., & Jdey, I. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21841-2
Metadata
- Authors
- Weimann, K., & Conrad, T. O. F.
- Affiliations
- Zuse Institute Berlin (DE)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2024-11
- Volume
- 28
- Issue
- 11
- Pages
- 6931-6943
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4400646195
- Cited by
- 19
- References
- 60
- Citations by year
- 2026: 9 · 2025: 10
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- German Ministry for Education and Research
- Berlin Institute for the Foundations of Learning and Data - BIFOLD (01IS18025A, 01IS18037I)
- Forschungscampus MODAL (3FO18501)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- federated learning, ECG classification, cardiac abnormality detection, ResNet, data privacy, out-of-distribution generalization, fine-tuning, data sharing
- MeSH terms
- Deep Learning, Electrocardiography, Humans, Signal Processing, Computer-Assisted, Databases, Factual, Neural Networks, Computer, Privacy
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