{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:30:16Z","timestamp":1742967016608,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031289958"},{"type":"electronic","value":"9783031289965"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-28996-5_10","type":"book-chapter","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T08:04:25Z","timestamp":1679990665000},"page":"130-143","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Two-Phased Federated Learning with\u00a0Clustering and\u00a0Personalization for\u00a0Natural Gas Load Forecasting"],"prefix":"10.1007","author":[{"given":"Shubao","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengxiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Briggs, C., Fan, Z., Andras, P.: Federated learning with hierarchical clustering of local updates to improve training on non-IID data. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20139. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"issue":"4","key":"10_CR2","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","volume":"35","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Qin, X., Wang, J., Yu, C., Gao, W.: FedHealth: a federated transfer learning framework for wearable healthcare. IEEE Intell. Syst. 35(4), 83\u201393 (2020)","journal-title":"IEEE Intell. Syst."},{"key":"10_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107669","volume":"137","author":"MN Fekri","year":"2022","unstructured":"Fekri, M.N., Grolinger, K., Mir, S.: Distributed load forecasting using smart meter data: federated learning with recurrent neural networks. Int. J. Electr. Power Energy Syst. 137, 107669 (2022)","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10_CR4","unstructured":"Guo, S., Li, Z., Liu, H., Zhao, S., Jin, C.H.: Personalized federated learning for multi-task fault diagnosis of rotating machinery. arXiv preprint arXiv:2211.09406 (2022)"},{"key":"10_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103291","volume":"99","author":"L Huang","year":"2019","unstructured":"Huang, L., Shea, A.L., Qian, H., Masurkar, A., Deng, H., Liu, D.: Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J. Biomed. Inform. 99, 103291 (2019)","journal-title":"J. Biomed. Inform."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Husnoo, M.A., Anwar, A., Hosseinzadeh, N., Islam, S.N., Mahmood, A.N., Doss, R.: FedREP: towards horizontal federated load forecasting for retail energy providers. arXiv preprint arXiv:2203.00219 (2022)","DOI":"10.1109\/APPEEC53445.2022.10072290"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Ji, S., Pan, S., Long, G., Li, X., Jiang, J., Huang, Z.: Learning private neural language modeling with attentive aggregation. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852464"},{"key":"10_CR8","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: SCAFFOLD: stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132\u20135143. PMLR (2020)"},{"issue":"7","key":"10_CR9","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.3390\/en11071687","volume":"11","author":"P Li","year":"2018","unstructured":"Li, P., Zhang, J.S.: A new hybrid method for China\u2019s energy supply security forecasting based on ARIMA and XGBoost. Energies 11(7), 1687 (2018)","journal-title":"Energies"},{"key":"10_CR10","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429\u2013450 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jngse.2021.103930","volume":"90","author":"J Liu","year":"2021","unstructured":"Liu, J., Wang, S., Wei, N., Chen, X., Xie, H., Wang, J.: Natural gas consumption forecasting: a discussion on forecasting history and future challenges. J. Nat. Gas Sci. Eng. 90, 103930 (2021)","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"10_CR12","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"10_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jngse.2021.104175","volume":"95","author":"S Peng","year":"2021","unstructured":"Peng, S., Chen, R., Yu, B., Xiang, M., Lin, X., Liu, E.: Daily natural gas load forecasting based on the combination of long short term memory, local mean decomposition, and wavelet threshold denoising algorithm. J. Nat. Gas Sci. Eng. 95, 104175 (2021)","journal-title":"J. Nat. Gas Sci. Eng."},{"issue":"3","key":"10_CR14","first-page":"1","volume":"7","author":"P Pradhan","year":"2016","unstructured":"Pradhan, P., Nayak, B., Dhal, S.K.: Time series data prediction of natural gas consumption using ARIMA model. Int. J. Inf. Technol. Manag. Inf. Syst. 7(3), 1\u20137 (2016)","journal-title":"Int. J. Inf. Technol. Manag. Inf. Syst."},{"issue":"8","key":"10_CR15","doi-asserted-by":"publisher","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","volume":"32","author":"F Sattler","year":"2020","unstructured":"Sattler, F., M\u00fcller, K.R., Samek, W.: Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans. Neural Netw. Learn. Syst. 32(8), 3710\u20133722 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"10_CR16","doi-asserted-by":"publisher","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","volume":"31","author":"F Sattler","year":"2019","unstructured":"Sattler, F., Wiedemann, S., M\u00fcller, K.R., Samek, W.: Robust and communication-efficient federated learning from non-IID data. IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3400\u20133413 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Ta\u00efk, A., Cherkaoui, S.: Electrical load forecasting using edge computing and federated learning. In: 2020 IEEE International Conference on Communications (ICC 2020), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/ICC40277.2020.9148937"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Toward personalized federated learning. In: IEEE Transactions on Neural Networks and Learning Systems (2022)","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"10_CR19","unstructured":"Wang, K., Mathews, R., Kiddon, C., Eichner, H., Beaufays, F., Ramage, D.: Federated evaluation of on-device personalization. arXiv preprint arXiv:1910.10252 (2019)"},{"key":"10_CR20","unstructured":"Wang, Y., Gao, N., Hug, G.: Personalized federated learning for individual consumer load forecasting. CSEE J. Power Energy Syst. (2022)"},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.apenergy.2019.05.023","volume":"250","author":"N Wei","year":"2019","unstructured":"Wei, N., Li, C., Peng, X., Li, Y., Zeng, F.: Daily natural gas consumption forecasting via the application of a novel hybrid model. Appl. Energy 250, 358\u2013368 (2019)","journal-title":"Appl. Energy"},{"key":"10_CR22","first-page":"22419","volume":"34","author":"J Xu","year":"2021","unstructured":"Xu, J., Wang, J., Long, M., et al.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural. Inf. Process. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"2","key":"10_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 1\u201319 (2019)","journal-title":"ACM Trans. Intell. Syst. Technol."}],"container-title":["Lecture Notes in Computer Science","Trustworthy Federated Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28996-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T08:08:36Z","timestamp":1679990916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28996-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031289958","9783031289965"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28996-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Trustworthy Federated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fl2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/federated-learning.org\/fl-ijcai-2022\/","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":"12","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":"0","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":"92% - 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":"2","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","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)"}}]}}