{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:51:07Z","timestamp":1780491067764,"version":"3.54.1"},"reference-count":150,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)","award":["06351"],"award-info":[{"award-number":["06351"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The \u201cdata hunger\u201d of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12\u201324 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.<\/jats:p>","DOI":"10.3390\/s23042112","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T01:41:06Z","timestamp":1676338866000},"page":"2112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":96,"title":["Reviewing Federated Machine Learning and Its Use in Diseases Prediction"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2860-5306","authenticated-orcid":false,"given":"Mohammad","family":"Moshawrab","sequence":"first","affiliation":[{"name":"D\u00e9partement de Math\u00e9matiques, Informatique et G\u00e9nie, Universit\u00e9 du Qu\u00e9bec \u00e0 Rimouski, 300 All\u00e9e des Ursulines, Rimouski, QC G5L 3A1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5327-1758","authenticated-orcid":false,"given":"Mehdi","family":"Adda","sequence":"additional","affiliation":[{"name":"D\u00e9partement de Math\u00e9matiques, Informatique et G\u00e9nie, Universit\u00e9 du Qu\u00e9bec \u00e0 Rimouski, 300 All\u00e9e des Ursulines, Rimouski, QC G5L 3A1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdenour","family":"Bouzouane","sequence":"additional","affiliation":[{"name":"D\u00e9partement d\u2019Informatique et de Math\u00e9matique, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, 555 Boulevard de l\u2019Universit\u00e9, Chicoutimi, QC G7H 2B1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9177-2967","authenticated-orcid":false,"given":"Hussein","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Institut Technologique de Maintenance Industrielle, 175 Rue de la V\u00e9rendrye, Sept-\u00celes, QC G4R 5B7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Raad","sequence":"additional","affiliation":[{"name":"Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"ref_1","unstructured":"Turing, A.M. (2009). Parsing the Turing Test, Springer."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Frankish, K., and Ramsey, W.M. (2014). The Cambridge Handbook of Artificial Intelligence, Cambridge University Press.","DOI":"10.1017\/CBO9781139046855"},{"key":"ref_3","unstructured":"Hern\u00e1ndez-Orallo, J., and Minaya-Collado, N. (1998, January 11\u201313). A formal definition of intelligence based on an intensional variant of algorithmic complexity. Proceedings of International Symposium of Engineering of Intelligent Systems (EIS98), Tenerife, Spain."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: Algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.gltp.2021.01.004","article-title":"Machine learning and deep learning applications-a vision","volume":"2","author":"Sharma","year":"2021","journal-title":"Glob. Transit. Proc."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pallathadka, H., Mustafa, M., Sanchez, D.T., Sajja, G.S., Gour, S., and Naved, M. (2021). Impact of machine learning on management, healthcare and agriculture. Mater. Today Proc., in press.","DOI":"10.1016\/j.matpr.2021.07.042"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ghazal, T.M., Hasan, M.K., Alshurideh, M.T., Alzoubi, H.M., Ahmad, M., Akbar, S.S., Al Kurdi, B., and Akour, I.A. (2021). IoT for smart cities: Machine learning approaches in smart healthcare\u2014A review. Future Internet, 13.","DOI":"10.3390\/fi13080218"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zantalis, F., Koulouras, G., Karabetsos, S., and Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11.","DOI":"10.3390\/fi11040094"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","article-title":"Machine learning and deep learning methods for cybersecurity","volume":"6","author":"Xin","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nagarhalli, T.P., Vaze, V., and Rana, N.K. (2021, January 4\u20136). Impact of machine learning in natural language processing: A review. Proceedings of the Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), IEEE, Tirunelveli, India.","DOI":"10.1109\/ICICV50876.2021.9388380"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Larra\u00f1aga, P., Atienza, D., Diaz-Rozo, J., Ogbechie, A., Puerto-Santana, C., and Bielza, C. (2018). Industrial Applications of Machine Learning, CRC Press.","DOI":"10.1201\/9781351128384"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7776","DOI":"10.1109\/ACCESS.2017.2696365","article-title":"Machine learning with big data: Challenges and approaches","volume":"5","author":"Grolinger","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","article-title":"Machine learning on big data: Opportunities and challenges","volume":"237","author":"Zhou","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Leskovec, J., Rajaraman, A., and Ullman, J.D. (2020). Mining of Massive Data Sets, Cambridge University Press.","DOI":"10.1017\/9781108684163"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3533378","article-title":"Challenges in deploying machine learning: A survey of case studies","volume":"55","author":"Paleyes","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1056\/NEJMp1714229","article-title":"Implementing machine learning in health care\u2014Addressing ethical challenges","volume":"378","author":"Char","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_19","first-page":"23","article-title":"Machine learning in manufacturing: Advantages, challenges, and applications","volume":"4","author":"Wuest","year":"2016","journal-title":"Prod. Manuf. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3299","DOI":"10.1007\/s10462-020-09948-w","article-title":"Machine learning towards intelligent systems: Applications, challenges, and opportunities","volume":"54","author":"Injadat","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"287","DOI":"10.21552\/EDPL\/2016\/3\/4","article-title":"How the GDPR will change the world","volume":"2","author":"Albrecht","year":"2016","journal-title":"Eur. Data Prot. L. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.clsr.2017.05.022","article-title":"The impact of China\u2019s 2016 Cyber Security Law on foreign technology firms, and on China\u2019s big data and Smart City dreams","volume":"34","author":"Parasol","year":"2018","journal-title":"Comput. Law Secur. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"715","DOI":"10.2307\/840330","article-title":"General Principles of Civil Law of the People\u2019s Republic of China","volume":"34","author":"Gray","year":"1986","journal-title":"Am. J. Comp. Law"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.clsr.2013.07.010","article-title":"The Singapore Personal Data Protection Act and an assessment of future trends in data privacy reform","volume":"29","author":"Chik","year":"2013","journal-title":"Comput. Law Secur. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006, January 4\u20137). Calibrating noise to sensitivity in private data analysis. Proceedings of the Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA.","DOI":"10.1007\/11681878_14"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1197\/jamia.M2716","article-title":"Protecting privacy using k-anonymity","volume":"15","author":"Dankar","year":"2008","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.future.2017.02.006","article-title":"Multi-key privacy-preserving deep learning in cloud computing","volume":"74","author":"Li","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., and Ristenpart, T. (2015, January 12\u201316). Model inversion attacks that exploit confidence information and basic countermeasures. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, CO, USA.","DOI":"10.1145\/2810103.2813677"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., and Shmatikov, V. (2017, January 22\u201326). Membership inference attacks against machine learning models. Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP), IEEE, San Jose, CA, USA.","DOI":"10.1109\/SP.2017.41"},{"key":"ref_30","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B.A. (2017, January 20\u201322). Communication-efficient learning of deep networks from decentralized data. Proceedings of the Artificial Intelligence and Statistics PMLR, Lauderdale, FL, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1308\/147870804290","article-title":"Artificial intelligence in medicine","volume":"86","author":"Ramesh","year":"2004","journal-title":"Ann. R. Coll. Surg. Engl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1001\/jama.2018.18932","article-title":"Questions for artificial intelligence in health care","volume":"321","author":"Maddox","year":"2019","journal-title":"JAMA"},{"key":"ref_33","unstructured":"Nayyar, A., Gadhavi, L., and Zaman, N. (2021). Machine Learning and the Internet of Medical Things in Healthcare, Academic Press."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., and Raad, A. (2023). Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. Sensors, 23.","DOI":"10.3390\/s23020828"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Makroum, M.A., Adda, M., Bouzouane, A., and Ibrahim, H. (2022). Machine learning and smart devices for diabetes management: Systematic review. Sensors, 22.","DOI":"10.3390\/s22051843"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., and Raad, A. (2022). Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review. Sensors, 22.","DOI":"10.3390\/s22197472"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of go without human knowledge","volume":"550","author":"Silver","year":"2017","journal-title":"Nature"},{"key":"ref_39","first-page":"1333","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"Aono","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_40","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.knosys.2021.106775","article-title":"A survey on federated learning","volume":"216","author":"Zhang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., and He, B. (2021). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Trans. Knowl. Data Eng.","DOI":"10.1109\/TKDE.2021.3124599"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"106854","DOI":"10.1016\/j.cie.2020.106854","article-title":"A review of applications in federated learning","volume":"149","author":"Li","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339474","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_45","unstructured":"Mammen, P.M. (2021). Federated learning: Opportunities and challenges. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"165817","DOI":"10.1007\/s11704-021-0598-z","article-title":"Challenges and future directions of secure federated learning: A survey","volume":"16","author":"Zhang","year":"2022","journal-title":"Front. Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Asad, M., Moustafa, A., and Ito, T. (2021). Federated Learning Versus Classical Machine Learning: A Convergence Comparison. arXiv.","DOI":"10.22541\/au.162074596.66890690\/v1"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mahlool, D.H., and Abed, M.H. (2022). A Comprehensive Survey on Federated Learning: Concept and Applications. arXiv.","DOI":"10.1007\/978-981-19-2069-1_37"},{"key":"ref_49","unstructured":"Zhang, H., Bosch, J., and Holmstr\u00f6m Olsson, H. (2020, January 16\u201318). Engineering Federated Learning Systems: A Literature Review. Proceedings of the 11th International Conference, ICSOB 2020, Karlskrona, Sweden."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: A comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41666-020-00082-4","article-title":"Federated learning for healthcare informatics","volume":"5","author":"Xu","year":"2021","journal-title":"J. Healthc. Inform. Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shokri, R., and Shmatikov, V. (2015, January 12\u201316). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, CO, USA.","DOI":"10.1145\/2810103.2813687"},{"key":"ref_53","first-page":"50","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lyu, L., Yu, H., and Yang, Q. (2020). Threats to federated learning: A survey. arXiv.","DOI":"10.1007\/978-3-030-63076-8_1"},{"key":"ref_55","unstructured":"Chen, X., Liu, C., Li, B., Lu, K., and Song, D. (2017). Targeted backdoor attacks on deep learning systems using data poisoning. arXiv."},{"key":"ref_56","unstructured":"Li, B., Wang, Y., Singh, A., and Vorobeychik, Y. (2016, January 5\u201310). Data poisoning attacks on factorization-based collaborative filtering. Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Alfeld, S., Zhu, X., and Barford, P. (2016, January 12\u201317). Data poisoning attacks against autoregressive models. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10237"},{"key":"ref_58","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V. (2020, January 3\u20135). How to backdoor federated learning. Proceedings of the International Conference on Artificial Intelligence and Statistics, PMLR, Sicily, Italy."},{"key":"ref_59","unstructured":"Xie, C., Huang, K., Chen, P.Y., and Li, B. (2019, January 18\u201320). Dba: Distributed backdoor attacks against federated learning. Proceedings of the International Conference on Learning Representations, Jakarta, Indonesia."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1145\/571637.571640","article-title":"Practical Byzantine fault tolerance and proactive recovery","volume":"20","author":"Castro","year":"2002","journal-title":"ACM Trans. Comput. Syst. (TOCS)"},{"key":"ref_61","unstructured":"Blanchard, P., El Mhamdi, E.M., Guerraoui, R., and Stainer, J. (2017, January 4\u20139). Machine learning with adversaries: Byzantine tolerant gradient descent. Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bayatbabolghani, F., and Blanton, M. (2018, January 15\u201319). Secure multi-party computation. Proceedings of the 2018 ACM SIGSAC conference on computer and communications security 2018, Toronto, Canada.","DOI":"10.1145\/3243734.3264419"},{"key":"ref_63","unstructured":"Dwork, C. (2008, January 25\u201319). Differential privacy: A survey of results. Proceedings of the International Conference on Theory and Applications of Models of Computation, Xi\u2019an, China."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"124682","DOI":"10.1109\/ACCESS.2021.3111118","article-title":"Challenges, applications and design aspects of federated learning: A survey","volume":"9","author":"Rahman","year":"2021","journal-title":"IEEE Access"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., and Seth, K. (November, January 30). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA.","DOI":"10.1145\/3133956.3133982"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TSP.2022.3153135","article-title":"Robust aggregation for federated learning","volume":"70","author":"Pillutla","year":"2022","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_68","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A.T. (2020, January 18\u201322). Scaffold: Stochastic controlled averaging for federated learning. Proceedings of the International Conference on Machine Learning, PMLR, Bangkok, Thailand."},{"key":"ref_69","unstructured":"Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Kone\u010dn\u00fd, J., Kumar, S., and McMahan, H.B. (2020). Adaptive federated optimization. arXiv."},{"key":"ref_70","unstructured":"Hamer, J., Mohri, M., and Suresh, A.T. (2020, January 18\u201322). Fedboost: A communication-efficient algorithm for federated learning. Proceedings of the International Conference on Machine Learning PMLR, Bangkok, Thailand."},{"key":"ref_71","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. (2020, January 2\u20134). Federated optimization in heterogeneous networks. Proceedings of the Machine Learning and Systems, Austin, TX, USA."},{"key":"ref_72","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y. (2020). Federated learning with matched averaging. arXiv."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/JIOT.2020.3002925","article-title":"Analog gradient aggregation for federated learning over wireless networks: Customized design and convergence analysis","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_74","unstructured":"Choi, B., Sohn, J.Y., Han, D.J., and Moon, J. (2020). Communication-computation efficient secure aggregation for federated learning. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"23920","DOI":"10.1109\/ACCESS.2020.2968399","article-title":"Federated learning in vehicular edge computing: A selective model aggregation approach","volume":"8","author":"Ye","year":"2020","journal-title":"IEEE Access"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/TPAMI.2020.3033286","article-title":"Lazily aggregated quantized gradient innovation for communication-efficient federated learning","volume":"44","author":"Sun","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/TC.2020.2994391","article-title":"SAFA: A semi-asynchronous protocol for fast federated learning with low overhead","volume":"70","author":"Wu","year":"2020","journal-title":"IEEE Trans. Comput."},{"key":"ref_78","unstructured":"Sannara, E.K., Portet, F., Lalanda, P., and German, V.E.G.A. (2021, January 22\u201326). A federated learning aggregation algorithm for pervasive computing: Evaluation and comparison. Proceedings of the 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom), IEEE, Kassel, Germany."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6804","DOI":"10.1109\/TWC.2021.3076613","article-title":"Dynamic aggregation for heterogeneous quantization in federated learning","volume":"20","author":"Chen","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Deng, Y., Lyu, F., Ren, J., Chen, Y.C., Yang, P., Zhou, Y., and Zhang, Y. (2021, January 10\u201313). Fair: Quality-aware federated learning with precise user incentive and model aggregation. Proceedings of the IEEE INFOCOM 2021\u2014IEEE Conference on Computer Communications, IEEE, Vancouver, BC, Canada.","DOI":"10.1109\/INFOCOM42981.2021.9488743"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Park, S., Suh, Y., and Lee, J. (2021). FedPSO: Federated learning using particle swarm optimization to reduce communication costs. Sensors, 21.","DOI":"10.3390\/s21020600"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Varma, K., Zhou, Y., Baracaldo, N., and Anwar, A. (2021, January 5\u201310). LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning. Proceedings of the 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), IEEE, Chicago, IL, USA.","DOI":"10.1109\/CLOUD53861.2021.00040"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.ins.2021.01.046","article-title":"MHAT: An efficient model-heterogenous aggregation training scheme for federated learning","volume":"560","author":"Hu","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Jeon, B., Ferdous, S.M., Rahman, M.R., and Walid, A. (2021, January 10\u201313). Privacy-preserving decentralized aggregation for federated learning. Proceedings of the IEEE INFOCOM 2021\u2014IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, Vancouver, BC, Canada.","DOI":"10.1109\/INFOCOMWKSHPS51825.2021.9484437"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Kantarci, B. (2021, January 14\u201323). Reputation-enabled federated learning model aggregation in mobile platforms. Proceedings of the ICC 2021\u2014IEEE International Conference on Communications, Montreal, QC, Canada.","DOI":"10.1109\/ICC42927.2021.9500928"},{"key":"ref_86","first-page":"2239","article-title":"Sear: Secure and efficient aggregation for byzantine-robust federated learning","volume":"19","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1109\/JSAIT.2021.3054610","article-title":"Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning","volume":"2","author":"So","year":"2021","journal-title":"IEEE J. Sel. Areas Inf. Theory"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Song, J., Wang, W., Gadekallu, T.R., Cao, J., and Liu, Y. (2022). Eppda: An efficient privacy-preserving data aggregation federated learning scheme. IEEE Trans. Netw. Sci. Eng., 1.","DOI":"10.1109\/TNSE.2022.3153519"},{"key":"ref_89","unstructured":"Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Malek, M., and Huba, D. (2022, January 28\u201330). Federated learning with buffered asynchronous aggregation. Proceedings of the International Conference on Artificial Intelligence and Statistics PMLR, Virtual Conference."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1109\/TCOMM.2022.3151126","article-title":"Heterosag: Secure aggregation with heterogeneous quantization in federated learning","volume":"70","author":"Elkordy","year":"2022","journal-title":"IEEE Trans. Commun."},{"key":"ref_91","unstructured":"So, J., Nolet, C.J., Yang, C.S., Li, S., Yu, Q., E Ali, R., Guler, B., and Avestimehr, S. (September, January 29). Lightsecagg: A lightweight and versatile design for secure aggregation in federated learning. Proceedings of the Machine Learning and Systems, Santa Clara, CA, USA."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1145\/96602.96604","article-title":"Federated database systems for managing distributed, heterogeneous, and autonomous databases","volume":"22","author":"Sheth","year":"1990","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_93","unstructured":"Kumar, Y., and Singla, R. (2021). Federated Learning Systems, Springer."},{"key":"ref_94","unstructured":"(2022, July 01). Google. 2019. TensorFlow Federated. Retrieved 1 July 2022. Available online: https:\/\/www.tensorflow.org\/federated."},{"key":"ref_95","first-page":"10320","article-title":"FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection","volume":"22","author":"Liu","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref_96","unstructured":"Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., and Passerat-Palmbach, J. (2018). A generic framework for privacy preserving deep learning. arXiv."},{"key":"ref_97","unstructured":"(2022, July 01). GitHub\u2014doc-ai\/tensorio: Declarative, On-Device Machine Learning for iOS, Android, and React Native. Deploy. Predict. Train. GitHub. Available online: https:\/\/github.com\/doc-ai\/tensorio."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501813","article-title":"Federated Learning for Healthcare: Systematic Review and Architecture Proposal","volume":"13","author":"Antunes","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Tan, K., Bremner, D., Le Kernec, J., and Imran, M. (2020, January 20\u201321). Federated machine learning in vehicular networks: A summary of recent applications. Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET), IEEE, Glasgow, UK.","DOI":"10.1109\/UCET51115.2020.9205482"},{"key":"ref_100","unstructured":"Liu, M., Ho, S., Wang, M., Gao, L., Jin, Y., and Zhang, H. (2021). Federated learning meets natural language processing: A survey. arXiv."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cell.2020.03.022","article-title":"How machine learning will transform biomedicine","volume":"181","author":"Goecks","year":"2020","journal-title":"Cell"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.procs.2022.07.030","article-title":"Cardiovascular Events Prediction using Artificial Intelligence Models and Heart Rate Variability","volume":"203","author":"Moshawrab","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ijmedinf.2018.01.007","article-title":"Federated learning of predictive models from federated electronic health records","volume":"112","author":"Brisimi","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Fang, L., Liu, X., Su, X., Ye, J., Dobson, S., Hui, P., and Tarkoma, S. (2020, January 19). Bayesian inference federated learning for heart rate prediction. Proceedings of the International Conference on Wireless Mobile Communication and Healthcare, Virtual Event.","DOI":"10.1007\/978-3-030-70569-5_8"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Brophy, E., De Vos, M., Boylan, G., and Ward, T. (2021). Estimation of continuous blood pressure from ppg via a federated learning approach. Sensors, 21.","DOI":"10.3390\/s21186311"},{"key":"ref_106","unstructured":"(2022, July 01). uff-Less Blood Pressure Estimation. (4 June 2017). Kaggle. Retrieved 1 July 2022. Available online: https:\/\/www.kaggle.com\/datasets\/mkachuee\/BloodPressureDataset."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1213\/ANE.0b013e318241f7c0","article-title":"University of Queensland vital signs dataset: Development of an accessible repository of anesthesia patient monitoring data for research","volume":"114","author":"Liu","year":"2012","journal-title":"Anesth. Analg."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"6217601","DOI":"10.1155\/2021\/6217601","article-title":"Personalized Federated Learning for ECG Classification Based on Feature Alignment","volume":"2021","author":"Tang","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_109","unstructured":"Lee, E.W., Xiong, L., Hertzberg, V.S., Simpson, R.L., and Ho, J.C. (2021, January 17). Privacy-preserving Sequential Pattern Mining in distributed EHRs for Predicting Cardiovascular Disease. Proceedings of the AMIA Summits on Translational Science Proceedings, Bethesda, MD, USA."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"107763","DOI":"10.1016\/j.knosys.2021.107763","article-title":"Designing ecg monitoring healthcare system with federated transfer learning and explainable AI","volume":"236","author":"Raza","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_111","unstructured":"(2022, July 01). MIT-BIH Arrhythmia Database v1.0.0. (24 February 2005). PhysioNet. Available online: https:\/\/physionet.org\/content\/mitdb\/1.0.0\/."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.1038\/s41598-022-07186-4","article-title":"Federated learning for multi-center imaging diagnostics: A simulation study in cardiovascular disease","volume":"12","author":"Linardos","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"3543","DOI":"10.1109\/TMI.2021.3090082","article-title":"Multi-centre, multi-vendor and multi-disease cardiac segmentation: The M&Ms challenge","volume":"40","author":"Campello","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_115","unstructured":"(2023, January 01). Diabetes. 2 December 2022. Available online: https:\/\/www.who.int\/health-topics\/diabetes#tab=tab_1."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"100069","DOI":"10.1016\/j.xops.2021.100069","article-title":"Federated learning for microvasculature segmentation and diabetic retinopathy classification of OCT data","volume":"1","author":"Lo","year":"2021","journal-title":"Ophthalmol. Sci."},{"key":"ref_117","first-page":"556","article-title":"A Federated Mining Approach on Predicting Diabetes-Related Complications: Demonstration Using Real-World Clinical Data","volume":"Volume 2021","author":"Islam","year":"2021","journal-title":"Proceedings of the AMIA Annual Symposium"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.future.2021.10.023","article-title":"Federated intelligence of anomaly detection agent in IoTMD-enabled Diabetes Management Control System","volume":"128","author":"Astillo","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Nielsen, C., Tuladhar, A., and Forkert, N.D. (2022, January 22). Investigating the Vulnerability of Federated Learning-Based Diabetic Retinopathy Grade Classification to Gradient Inversion Attacks. Proceedings of the International Workshop on Ophthalmic Medical Image Analysis, Singapore.","DOI":"10.1007\/978-3-031-16525-2_19"},{"key":"ref_120","unstructured":"(2023, January 12). \u201cFGADR Dataset\u2014Look Deeper Into Eyes.\u201d FGADR Dataset\u2014Look Deeper Into Eyes.|FGADR. Available online: csyizhou.github.io\/FGADR\/blob\/NateBYWang-patch-1\/\/FGADR."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Liu, J., Lu, X., Yang, H., and Zhuang, L. (2022, January 20\u201322). A Diabetes Prediction System Based on Federated Learning. Proceedings of the 2022 International Conference on Big Data, Information and Computer Network (BDICN), IEEE, Sanya, China.","DOI":"10.1109\/BDICN55575.2022.00095"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Nasajpour, M., Karakaya, M., Pouriyeh, S., and Parizi, R.M. (April, January 26). Federated Transfer Learning For Diabetic Retinopathy Detection Using CNN Architectures. Proceedings of the SoutheastCon 2022, IEEE, Mobile, AL, USA.","DOI":"10.1109\/SoutheastCon48659.2022.9764031"},{"key":"ref_123","unstructured":"Cuadros, J., and Sim, I. (2004). EyePACS: An open source clinical communication system for eye care. Stud. Health Technol. Inform., 207\u2013211."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a publicly distributed image database: The Messidor database","volume":"33","author":"Zhang","year":"2014","journal-title":"Image Anal. Stereol."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., and Meriaudeau, F. (2018). Indian diabetic retinopathy image dataset (IDRiD): A database for diabetic retinopathy screening research. Data, 3.","DOI":"10.3390\/data3030025"},{"key":"ref_126","unstructured":"APTOS 2019 Blindness Detection | Kaggle (2023, January 12). APTOS 2019 Blindness Detection | Kaggle. Available online: https:\/\/www.kaggle.com\/c\/aptos2019-blindness-detection."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Chalakkal, R.J., Abdulla, W.H., and Sinumol, S. (2017, January 27\u201330). Comparative analysis of university of Auckland diabetic retinopathy database. Proceedings of the 9th International Conference on Signal Processing Systems, Auckland, New Zealand.","DOI":"10.1145\/3163080.3163087"},{"key":"ref_128","unstructured":"(2023, January 13). Cancer. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Chowdhury, A., Kassem, H., Padoy, N., Umeton, R., and Karargyris, A. (2021, January 27). A Review of Medical Federated Learning: Applications in Oncology and Cancer Research. Proceedings of the International MICCAI Brainlesion Workshop, Virtual Event.","DOI":"10.1007\/978-3-031-08999-2_1"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Yi, L., Zhang, J., Zhang, R., Shi, J., Wang, G., and Liu, X. (2020, January 15\u201318). SU-Net: An efficient encoder-decoder model of federated learning for brain tumor segmentation. Proceedings of the International Conference on Artificial Neural Networks, Bratislava, Slovakia.","DOI":"10.1007\/978-3-030-61609-0_60"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11060-017-2420-1","article-title":"Radiogenomics of lower-grade glioma: Algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data","volume":"133","author":"Mazurowski","year":"2017","journal-title":"J.-Neuro-Oncol."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Sheller, M.J., Reina, G.A., Edwards, B., Martin, J., and Bakas, S. (2018, January 16). Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. Proceedings of the International MICCAI Brainlesion Workshop, Granada, Spain.","DOI":"10.1007\/978-3-030-11723-8_9"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-69250-1","article-title":"Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"Sheller","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"vi176","DOI":"10.1093\/neuonc\/noz175.737","article-title":"NIMG-68. Federated Learning in Neuro-Oncology for Multi-Institutional Collaborations without Sharing Patient Data","volume":"21","author":"Sheller","year":"2019","journal-title":"Neuro-Oncol."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1109\/TII.2021.3093715","article-title":"A many-objective optimization based federal deep generation model for enhancing data processing capability in IoT","volume":"19","author":"Cai","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., and Halpern, A. (2018, January 4\u20137). Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Hashmani, M.A., Jameel, S.M., Rizvi, S.S.H., and Shukla, S. (2021). An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device. Appl. Sci., 11.","DOI":"10.3390\/app11052145"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Roth, H.R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., and Kalpathy-Cramer, J. (2020, January 4\u20138). Federated learning for breast density classification: A real-world implementation. Proceedings of the Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, Lima, Peru.","DOI":"10.1007\/978-3-030-60548-3_18"},{"key":"ref_140","unstructured":"Rooijakkers, T. (2020). CONVINCED\u2014Enabling Privacy-Preserving Survival Analyses Using Multi-Party Computation, TNO."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1093\/jamia\/ocaa341","article-title":"Federated learning improves site performance in multicenter deep learning without data sharing","volume":"28","author":"Sarma","year":"2021","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.radonc.2019.11.019","article-title":"Distributed learning on 20 000+ lung cancer patients\u2014The Personal Health Train","volume":"144","author":"Deist","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Wang, P., Shen, C., Roth, H.R., Yang, D., Xu, D., Oda, M., Misawa, K., Chen, P.-T., Liu, K.-L., and Mori, K. (2020, January 4\u20138). Automated pancreas segmentation using multi-institutional collaborative deep learning. Proceedings of the Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, MICCAI 2020, Lima, Peru.","DOI":"10.1007\/978-3-030-60548-3_19"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"e25869","DOI":"10.2196\/25869","article-title":"Federated learning for thyroid ultrasound image analysis to protect personal information: Validation study in a real health care environment","volume":"9","author":"Lee","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.radonc.2021.03.013","article-title":"Predicting outcomes in anal cancer patients using multi-centre data and distributed learning\u2014A proof-of-concept study","volume":"159","author":"Choudhury","year":"2021","journal-title":"Radiother. Oncol."},{"key":"ref_146","first-page":"19","article-title":"Federated learning: Applications, challenges and future directions","volume":"18","author":"Bharati","year":"2022","journal-title":"Int. J. Hybrid Intell. Syst."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3453476","article-title":"Federated learning for smart healthcare: A survey","volume":"55","author":"Nguyen","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450288","article-title":"A systematic literature review on federated machine learning: From a software engineering perspective","volume":"54","author":"Lo","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_149","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V. (2018). Federated learning with non-iid data. arXiv."},{"key":"ref_150","unstructured":"Jiang, Y., Kone\u010dn\u00fd, J., Rush, K., and Kannan, S. (2019). Improving federated learning personalization via model agnostic meta learning. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:34:01Z","timestamp":1760121241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,13]]},"references-count":150,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23042112"],"URL":"https:\/\/doi.org\/10.3390\/s23042112","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,13]]}}}