{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:31:07Z","timestamp":1756992667514,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031492518"},{"type":"electronic","value":"9783031492525"}],"license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-49252-5_4","type":"book-chapter","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T06:02:12Z","timestamp":1701151332000},"page":"26-41","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Federated Learning Algorithms Development Paradigm"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8385-149X","authenticated-orcid":false,"given":"Miroslav","family":"Popovic","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1957-0092","authenticated-orcid":false,"given":"Marko","family":"Popovic","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3417-7237","authenticated-orcid":false,"given":"Ivan","family":"Kastelan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7563-3820","authenticated-orcid":false,"given":"Miodrag","family":"Djukic","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8824-5560","authenticated-orcid":false,"given":"Ilija","family":"Basicevic","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"4_CR1","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In 20th International Conference on Artificial Intelligence and Statistics, vol. 54, pp. 1273\u20131282. PMLR (2017)"},{"key":"4_CR2","unstructured":"TensorFlow Federated: Machine Learning on Decentralized Data. https:\/\/www.tensorflow.org\/federated. Accessed 01 Sept 2023"},{"key":"4_CR3","unstructured":"Federated Learning from Research to Practice. https:\/\/www.pdl.cmu.edu\/SDI\/2019\/slides\/2019-09-05Federated%20Learning.pdf. Accessed 01 Sept 2023"},{"issue":"167","key":"4_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s21010167","volume":"21","author":"I Kholod","year":"2021","unstructured":"Kholod, I., et al.: Open-source federated learning frameworks for IoT: a comparative review and analysis. Sensors 21(167), 1\u201322 (2021). https:\/\/doi.org\/10.3390\/s21010167","journal-title":"Sensors"},{"key":"4_CR5","doi-asserted-by":"publisher","unstructured":"Popovic, M., Popovic, M., Kastelan, I., Djukic, M., Ghilezan, S.: A simple Python testbed for federated learning algorithms. In: 2023 Zooming Innovation in Consumer Technologies Conference, Piscataway, New Jersey, USA, pp. 148\u2013153. IEEE Xplore (2023). https:\/\/doi.org\/10.1109\/ZINC58345.2023.10173859","DOI":"10.1109\/ZINC58345.2023.10173859"},{"key":"4_CR6","doi-asserted-by":"publisher","unstructured":"Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175\u20131191. ACM, New York (2017). https:\/\/doi.org\/10.1145\/3133956.3133982","DOI":"10.1145\/3133956.3133982"},{"key":"4_CR7","unstructured":"Konecny, J., McMahan, H.B., Yu, F.X., Suresh, A.T., Bacon, D., Richtarik, P.: Federated Learning: strategies for improving communication efficiency. arXiv, Cornell University (2017). https:\/\/arxiv.org\/abs\/1610.05492"},{"issue":"4","key":"4_CR8","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1145\/3500240","volume":"65","author":"K Bonawitz","year":"2022","unstructured":"Bonawitz, K., Kairouz, P., McMahan, B., Ramage, D.: Federated learning and privacy. Commun. ACM 65(4), 90\u201397 (2022). https:\/\/doi.org\/10.1145\/3500240","journal-title":"Commun. ACM"},{"issue":"4","key":"4_CR9","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1145\/3512343","volume":"65","author":"D Perino","year":"2022","unstructured":"Perino, D., Katevas, K., Lutu, A., Marin, E., Kourtellis, N.: Privacy-preserving AI for future networks. Commun. ACM 65(4), 52\u201353 (2022). https:\/\/doi.org\/10.1145\/3512343","journal-title":"Commun. ACM"},{"key":"4_CR10","unstructured":"Ying, B., Yuan, K., Hu, H., Chen, Y., Yin, W.: BlueFog: make decentralized algorithms practical for optimization and deep learning. arXiv, Cornell University (2021). https:\/\/arxiv.org\/abs\/2111.04287"},{"key":"4_CR11","unstructured":"Ying, B., Yuan, K., Chen, Y., Hu, H., Pan, P., Yin, W.: Exponential graph is provably efficient for decentralized deep training. arXiv, Cornell University (2021). https:\/\/arxiv.org\/abs\/2110.13363"},{"key":"4_CR12","unstructured":"An Industrial Grade Federated Learning Framework. https:\/\/fate.fedai.org\/. Accessed 01 Sept 2023"},{"key":"4_CR13","unstructured":"An Open-Source Deep Learning Platform Originated from Industrial Practice. https:\/\/www.paddlepaddle.org.cn\/en. Accessed 01 Sept 2023"},{"key":"4_CR14","unstructured":"A world where every good question is answered. https:\/\/www.openmined.org. Accessed 01 Sept 2023"},{"key":"4_CR15","unstructured":"Privacy-Preserving Artificial Intelligence to advance humanity. https:\/\/sherpa.ai. Accessed 01 Sept 2023"},{"key":"4_CR16","unstructured":"Deploy machine learning models on mobile and edge devices. https:\/\/www.tensorflow.org\/lite. Accessed 01 Sept 2023"},{"key":"4_CR17","unstructured":"David, R., et al.: TensorFlow lite micro: embedded machine learning on TinyML systems. arXiv, Cornell University (2021). https:\/\/arxiv.org\/abs\/2010.08678"},{"key":"4_CR18","unstructured":"PyTorch Mobile. End-to-end workflow from Training to Deployment for iOS and Android mobile devices. https:\/\/pytorch.org\/mobile\/home\/. Accessed 01 Sept 2023"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: 33rd International Conference on Neural Information Processing Systems, Article 721, pp. 8026\u20138037. ACM, New York (2019). https:\/\/doi.org\/10.5555\/3454287.3455008","DOI":"10.5555\/3454287.3455008"},{"key":"4_CR20","unstructured":"Luo, C., He, X., Zhan, J., Wang, L., Gao, W., Dai, J.: Comparison and benchmarking of AI models and frameworks on mobile devices. arXiv, Cornell University (2020). https:\/\/arxiv.org\/abs\/2005.05085"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Feraudo, A., et al.: CoLearn: enabling federated learning in MUD-compliant IoT Edge Networks. In: 3rd International Workshop on Edge Systems, Analytics and Networking, pp. 25\u201330. ACM, New York (2020). https:\/\/doi.org\/10.1145\/3378679.3394528","DOI":"10.1145\/3378679.3394528"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Zhang, T., He, C., Ma, T., Gao, L., Ma, M., Avestimehr, S.: Federated learning for Internet of Things. In: 19th ACM Conference on Embedded Networked Sensor Systems, pp. 413\u2013419. ACM, New York (2021). https:\/\/doi.org\/10.1145\/3485730.3493444","DOI":"10.1145\/3485730.3493444"},{"key":"4_CR23","doi-asserted-by":"publisher","unstructured":"Shen, C., Xue, W.: An experiment study on federated learning testbed. In: Zhang, Y.D., Senjyu, T., So-In, C., Joshi, A. (eds.) Smart Trends in Computing and Communications. LNNS, vol. 286, pp. 209\u2013217. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-4016-2_20","DOI":"10.1007\/978-981-16-4016-2_20"},{"key":"4_CR24","volume-title":"Patterns for Parallel Programming","author":"TG Mattson","year":"2008","unstructured":"Mattson, T.G., Sanders, B., Massingill, B.: Patterns for Parallel Programming. Addison-Wesley, Massachusetts, USA (2008)"},{"key":"4_CR25","unstructured":"Logistic Regression. https:\/\/colab.research.google.com\/drive\/1qmdfU8tzZ08D3O84qaD11Ffl9YuNUvlD. Accessed 01 Sept 2023"},{"issue":"243","key":"4_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/a15070243","volume":"15","author":"M Cellamare","year":"2022","unstructured":"Cellamare, M., van Gestel, A.J., Alradhi, H., Martin, F., Moncada-Torres, A.: A federated generalized linear model for privacy-preserving analysis. Algorithms 15(243), 1\u201312 (2022). https:\/\/doi.org\/10.3390\/a15070243","journal-title":"Algorithms"}],"container-title":["Lecture Notes in Computer Science","Engineering of Computer-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49252-5_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T00:05:47Z","timestamp":1701734747000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49252-5_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,29]]},"ISBN":["9783031492518","9783031492525"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49252-5_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,29]]},"assertion":[{"value":"29 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECBS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering of Computer-Based Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"V\u00e4ster\u00e5s","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecbseerc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.researchr.org\/home\/ecbs-2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.42","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.02","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}