{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:34:46Z","timestamp":1742913286701,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":57,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819600250"},{"type":"electronic","value":"9789819600267"}],"license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0026-7_16","type":"book-chapter","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T19:01:56Z","timestamp":1731697316000},"page":"203-213","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Integration of Federated Learning Techniques in Predictive Aircraft Maintenance Using Cloud Services"],"prefix":"10.1007","author":[{"given":"Kim","family":"Tigchelaar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyed Sahand","family":"Mohammadi Ziabari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeroen","family":"Mulder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"16_CR1","unstructured":"CMAPSS Jet Engine Simulated Data: NASA Open Data Portal (2022). https:\/\/data.nasa.gov\/Aerospace\/CMAPSS-Jet-Engine-Simulated-Data\/ff5vkuh6\/about_data"},{"key":"16_CR2","unstructured":"Federated learning on Google Cloud (2022). https:\/\/cloud.google.com\/architecture\/federated-learning-google-cloud"},{"key":"16_CR3","unstructured":"Cloud computing services - Amazon Web Services (AWS) (2024). https:\/\/aws.amazon.com\/"},{"key":"16_CR4","unstructured":"Cloud-Computing-Services: Microsoft Azure (2024). https:\/\/azure.microsoft.com\/nl-nl"},{"key":"16_CR5","unstructured":"Flower Framework (2024). https:\/\/flower.ai\/docs\/framework\/tutorial-series-usea-federated-learning-strategy-pytorch.html"},{"key":"16_CR6","unstructured":"Google Cloud (2024). https:\/\/cloud.google.com\/?hl=nl"},{"key":"16_CR7","unstructured":"TensorFlow federated (2024). https:\/\/www.tensorflow.org\/federated"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"An, D., Kim, N.H., Choi, J..: Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliab. Eng. Syst. Saf. 133, 223\u2013236 (2015)","DOI":"10.1016\/j.ress.2014.09.014"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Beltr\u00e1n, E.T.M., et al.: Decentralized federated learning: fundamentals, state of the art, frameworks, trends, and challenges. IEEE Commun. Surv. Tutorials 25, 2983\u20133013 (2023)","DOI":"10.1109\/COMST.2023.3315746"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Bemani, A., Bj\u00f6rsell, N.: Aggregation strategy on federated machine learning algorithm for collaborative predictive maintenance. Sensors 22(16), 6252 (2022)","DOI":"10.3390\/s22166252"},{"key":"16_CR11","unstructured":"Bendigeri, P., Air France-KLM: Value of a generic data exchange based data marketplace using federated learning (2023)"},{"key":"16_CR12","unstructured":"Beutel, D.J., et al.: Flower: a friendly federated learning framework. Hal (2022)"},{"key":"16_CR13","unstructured":"Braungardt, A.: FATE, Flower, PySyft Co. \u2014 Federated Learning Frameworks in Python (2023). https:\/\/medium.com\/elca-it\/flower-pysyft-cofederated-learning-frameworks-in-python-b1a8eda68b0d"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Broer, A., Benedictus, R., Zarouchas, D.: The need for multi-sensor data fusion in structural health monitoring of composite aircraft structures. Aerospace 9(4), 183 (2022)","DOI":"10.3390\/aerospace9040183"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Brum, R.C., Arantes, L., Castro, M.C., Sens, P., Drummond, L.M.A.: Evaluating execution times and costs of a federated learning application on different cloud providers. In: COMPAS 2022-Conf\u00e9rence Francophone d\u2019informatique en Parall\u00e9lisme, Architecture et Syst\u00e8me (2022)","DOI":"10.1109\/SBAC-PAD55451.2022.00036"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Chao, M.A., Kulkarni, C.S., Goebel, K., Fink, O.: Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics. Data 6(1), 5 (2021)","DOI":"10.3390\/data6010005"},{"key":"16_CR17","unstructured":"Charles, Z., Google: Talk on adaptive federated optimization (2021). https:\/\/blog.openmined.org\/adaptive-federated-optimization\/"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Che, C., Wang, H., Fu, Q., Ni, X.: Combining multiple deep learning algorithms for prognostic and health management of aircraft. Aerosp. Sci. Technol. 94, 105423 (2019)","DOI":"10.1016\/j.ast.2019.105423"},{"key":"16_CR19","unstructured":"Wikipedia contributors: Aircraft maintenance (2023). https:\/\/en.wikipedia.org\/wiki\/Aircraft_maintenance"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Drainakis, G., Katsaros, K.V., Pantazopoulos, P., Sourlas, V., Amditis, A.: Federated vs. centralized machine learning under privacy-elastic users: a comparative analysis. In: 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA) (2020)","DOI":"10.1109\/NCA51143.2020.9306745"},{"key":"16_CR21","unstructured":"Educative: What is federated averaging (FedAvg)? (2024). https:\/\/www.educative.io\/answers\/what-is-federated-averaging-fedavg"},{"key":"16_CR22","unstructured":"FedAI: Overview (2024). https:\/\/fate.fedai.org\/overview\/"},{"key":"16_CR23","unstructured":"Flower: Flower Framework Strategies (2024). https:\/\/flower.ai\/docs\/framework\/ref-api\/flwr.server.strategy.html#module-flwr.server.strategy"},{"key":"16_CR24","unstructured":"Goebel, K., Celaya, J., Sankararaman, S., Saxena, A.: Prognostics: The Science of Making Predictions. ResearchGate (2017). https:\/\/www.researchgate.net\/publication\/315773020_Prognostics_The_Science_of_Making_Predictions"},{"key":"16_CR25","unstructured":"Air France-KLM Group: The group | AIR FRANCE KLM (2024). https:\/\/www.airfranceklm.com\/en\/group"},{"key":"16_CR26","doi-asserted-by":"publisher","unstructured":"Guendouzi, B.S., Ouchani, S., Assaad, H.E.L., Zaher, M.E.L.: A systematic review of federated learning: challenges, aggregation methods, and development tools. J. Netw. Comput. Appl. 220, 103714 (2023). https:\/\/doi.org\/10.1016\/j.jnca.2023.103714","DOI":"10.1016\/j.jnca.2023.103714"},{"key":"16_CR27","unstructured":"Hard, A., et al.: Federated learning for mobile keyboard prediction. arXiv (Cornell University) arXiv:1811.03604 (2018)"},{"key":"16_CR28","unstructured":"Huang, C.: Cross-silo federated learning: challenges and opportunities (2022). https:\/\/arxiv.org\/abs\/2206.12949#:~:text=Based%20on%20the%20participating%20clients,and%20the%20client%20number%20is"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"James, G., et al.: Tree-based methods. Technical Report, pp. 331\u2013334 (2023). https:\/\/datamineaz.org\/readings\/ISL_chp8.1.pdf","DOI":"10.1007\/978-3-031-38747-0_8"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Jia, Z., Xiao, Z., Shi, Y.: Remaining useful life prediction of equipment based on XGBoost. In: Proceedings of the 5th International Conference on Computer Science and Application Engineering (CSAE 2021), pp. 1\u20136 (2021)","DOI":"10.1145\/3487075.3487134"},{"key":"16_CR31","doi-asserted-by":"publisher","unstructured":"Kholod, I.: Open-source federated learning frameworks for IoT: a comparative review and analysis. Sensors 21(1), 167 (2020). https:\/\/doi.org\/10.3390\/s21010167","DOI":"10.3390\/s21010167"},{"key":"16_CR32","unstructured":"Kone\u010dn\u00fd, J.: Federated optimization: distributed machine learning for on-device intelligence (2016). https:\/\/arxiv.org\/abs\/1610.02527"},{"key":"16_CR33","unstructured":"Kumarapu, L.: Tackling Non-independent and identically distributed data in Federated Learning (2022)"},{"key":"16_CR34","unstructured":"Lin, Z., et al.: Open-source AI-based SE tools: opportunities and challenges of collaborative software learning. Technical Report (2024)"},{"key":"16_CR35","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20\u201322 April 2017, Fort Lauderdale, FL, USA, pp. 1273\u20131282 (2017)"},{"key":"16_CR36","unstructured":"Mittal, S.: Federated Learning with PySyft - Towards Data Science (2021)"},{"key":"16_CR37","unstructured":"Moreno, A.I.: Data normalization with Pandas and Scikit-Learn - Towards Data Science (2021)"},{"key":"16_CR38","unstructured":"MyFlyRight: Predictive maintenance in the airline industry (2023)"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Ochella, S., Shafiee, M., Dinmohammadi, F.: Artificial intelligence in prognostics and health management of engineering systems. Eng. Appl. Artif. Intell. 108, 104552 (2022)","DOI":"10.1016\/j.engappai.2021.104552"},{"key":"16_CR40","unstructured":"OECD.AI: Root mean squared error (RMSE) (2024)"},{"key":"16_CR41","unstructured":"Argemi, A.P.: Design, implementation and analysis of a cloud federated learning architecture. Master\u2019s thesis. Universitat Polit\u00e8cnica de Catalunya (2023)"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Protopapadakis, G., Apostolidis, A., Kalfas, A.I.: Explainable and interpretable AI-assisted remaining useful life estimation for aeroengines. In: ASME Turbo Expo 2022 (2022)","DOI":"10.1115\/GT2022-80777"},{"key":"16_CR43","unstructured":"PySyft: PySyft. https:\/\/blog.openmined.org\/tag\/pysyft\/"},{"key":"16_CR44","unstructured":"Reddi, S.J., et al: Adaptive federated optimization (2021). https:\/\/arxiv.org\/pdf\/2003.00295v5"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Riedel, P., Reichert, M., Schweirin, R., Hafner, A., Schnaudt, D., Singh, G.: Performance analysis of federated learning algorithms for multilingual protest news detection using Pre-Trained DistilBERT and BERT. IEEE Access 11, 134009-134022 (2023)","DOI":"10.1109\/ACCESS.2023.3334910"},{"key":"16_CR46","unstructured":"SAP: Root Mean Squared Error (RMSE)"},{"key":"16_CR47","unstructured":"Singh, S.: PPML series 2 - federated optimization algorithms - FEDSGD and FedAVG (2021). https:\/\/shreyansh26.github.io\/post\/2021-12-18_federated_optimization_fedavg\/"},{"key":"16_CR48","doi-asserted-by":"crossref","unstructured":"Solanki, T., Kumar, B., Sharma, S.: Federated Learning Using Tensor Flow, pp. 157\u2013167 (2022)","DOI":"10.1007\/978-3-030-85559-8_10"},{"key":"16_CR49","unstructured":"Stefanov, S.: Automating the centralized-to-federated transition for the NASA C-MAPSS Dataset"},{"issue":"2021","key":"16_CR50","first-page":"1333","volume":"18","author":"Z Su","year":"2021","unstructured":"Su, Z., Yuntao Wang, T., Luan, N.Z., Li, F., Chen, T., Cao, H.: Secure and efficient federated learning for smart grid with edge-cloud collaboration. IEEE Trans. Industr. Inf. 18(2021), 1333\u20131344 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"16_CR51","unstructured":"Tari, A.: Comparative analysis of federated learning aggregation techniques for Alzheimer\u2019s disease diagnosis. Int. Res. J. Eng. Technol. (IRJET) 862 (2024). https:\/\/irjet.com\/archives\/V11\/i5\/IRJET-V11I5119.pdf"},{"key":"16_CR52","unstructured":"Taylor, S.: R-Squared (2023). https:\/\/corporatefinanceinstitute.com\/resources\/data-science\/r-squared\/#:~:text=R%2DSquared%20(R%C2%B2%20or%20the,(the%20goodness%20of%20fit)"},{"key":"16_CR53","unstructured":"TensorFlow: TensorFlow (2024). https:\/\/www.tensorflow.org\/"},{"key":"16_CR54","doi-asserted-by":"publisher","unstructured":"Volponi, A.J.: Gas turbine parameter corrections. J. Eng. Gas Turbines Power 121(4), 613\u2013621 (1999). https:\/\/doi.org\/10.1115\/1.2818516","DOI":"10.1115\/1.2818516"},{"key":"16_CR55","doi-asserted-by":"crossref","unstructured":"Witt, L., Heyer, M., Toyoda, K., Samek, W., Li, D.: Decentral and incentivized federated learning frameworks: a systematic literature review. IEEE Internet Things J. 10(4), 3642\u20133663 (2023)","DOI":"10.1109\/JIOT.2022.3231363"},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Wu, X., Huang, F., Hu, Z., Huang, H.: Faster adaptive federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence 37(9), 10379\u201310387 (2023)","DOI":"10.1609\/aaai.v37i9.26235"},{"issue":"2022","key":"16_CR57","first-page":"988","volume":"20","author":"Y Zheng","year":"2022","unstructured":"Zheng, Y., Lai, S., Liu, Y., Xingliang, X., Yi, X., Wang, C.: Aggregation service for federated learning: an efficient, secure, and more resilient realization. IEEE Trans. Dependable Secure Comput. 20(2022), 988\u20131001 (2022)","journal-title":"IEEE Trans. Dependable Secure Comput."}],"container-title":["Lecture Notes in Computer Science","Knowledge Management and Acquisition for Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0026-7_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T19:04:26Z","timestamp":1731697466000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0026-7_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"ISBN":["9789819600250","9789819600267"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0026-7_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,16]]},"assertion":[{"value":"16 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PKAW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Principle and Practice of Data and Knowledge Acquisition Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pkaw2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pkawwebsite.github.io\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}