{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:14:09Z","timestamp":1742922849883,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030977733"},{"type":"electronic","value":"9783030977740"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-97774-0_14","type":"book-chapter","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T08:06:40Z","timestamp":1647245200000},"page":"154-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Accuracy and Resource Allocation for Green Federated Learning Networks"],"prefix":"10.1007","author":[{"given":"Xu","family":"Chu","sequence":"first","affiliation":[]},{"given":"Xiaoyang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qimei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yunfei","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Juanjuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Han","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.inffus.2019.07.012","volume":"55","author":"H Qiu","year":"2020","unstructured":"Qiu, H., Qiu, M., Lu, Z.: Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf. Fusion 55, 59\u201367 (2020)","journal-title":"Inf. Fusion"},{"doi-asserted-by":"crossref","unstructured":"Qiu, L., Gai, K., Qiu, M.: Optimal big data sharing approach for tele-health in cloud computing. In: IEEE SmartCloud, pp. 184\u2013189 (2016)","key":"14_CR2","DOI":"10.1109\/SmartCloud.2016.21"},{"issue":"9","key":"14_CR3","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1109\/TKDE.2018.2866863","volume":"31","author":"R Lu","year":"2018","unstructured":"Lu, R., Jin, X., Zhang, S., Qiu, M., Wu, X.: A study on big knowledge and its engineering issues. IEEE Trans. Knowl. Data Eng. 31(9), 1630\u20131644 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"unstructured":"Zhu, G., Liu, D., Du, Y., You, C., Zhang, J., Huang, K.: Towards an intelligent edge: wireless communication meets machine learning (2018). arXiv:1809.00343","key":"14_CR4"},{"unstructured":"Bonawitz, K., et al.: Towards federated learning at scale: system design. In: Proceedings of Systems and Machine Learning Conference, Stanford, CA, USA, pp. 1\u201315 (2019)","key":"14_CR5"},{"unstructured":"McMahan, H.B., Moore, E., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)","key":"14_CR6"},{"key":"14_CR7","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.future.2019.04.039","volume":"99","author":"X Zhao","year":"2019","unstructured":"Zhao, X., Yang, K., Chen, Q., et al.: Deep learning based mobile data offloading in mobile edge computing systems. Future Gener. Comput. Syst. 99, 346\u2013355 (2019)","journal-title":"Future Gener. Comput. Syst."},{"doi-asserted-by":"crossref","unstructured":"Yang, Y., Chen, Q.: Distributed resource allocation under mobile edge computing networks: invited paper. In: 17th ISWCS, pp. 1\u20136 (2021)","key":"14_CR8","DOI":"10.1109\/ISWCS49558.2021.9562227"},{"unstructured":"Kone\u010dn\u00fd, J., McMahan, B., Ramage, D.: Federated optimization: distributed optimization beyond the datacenter, November 2015. arXiv:1511.03575","key":"14_CR9"},{"issue":"3","key":"14_CR10","doi-asserted-by":"publisher","first-page":"1935","DOI":"10.1109\/TWC.2020.3037554","volume":"20","author":"Z Yang","year":"2021","unstructured":"Yang, Z., Chen, M., Saad, W., Hong, C.S., Shikh-Bahaei, M.: Energy efficient federated learning over wireless communication networks. IEEE Trans. Wirel. Comm. 20(3), 1935\u20131949 (2021)","journal-title":"IEEE Trans. Wirel. Comm."},{"doi-asserted-by":"crossref","unstructured":"Hu, Y., Huang, H., Yu, N.: Device scheduling for energy-efficient federated learning over wireless network based on TDMA mode. In: International Conference on WCSP, pp. 286\u2013291 (2020)","key":"14_CR11","DOI":"10.1109\/WCSP49889.2020.9299815"},{"doi-asserted-by":"crossref","unstructured":"Tran, N.H., Bao, W., Zomaya, A., Nguyen, M., Hong, C.S.: Federated learning over wireless networks: optimization model design and analysis. In: IEEE INFOCOM 2019, pp. 1387\u20131395 (2019)","key":"14_CR12","DOI":"10.1109\/INFOCOM.2019.8737464"},{"issue":"2","key":"14_CR13","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1109\/TWC.2020.3031503","volume":"20","author":"J Xu","year":"2021","unstructured":"Xu, J., Wang, H.: Client selection and bandwidth allocation in wireless federated learning networks: a long-term perspective. IEEE Trans. Wirel. Comm. 20(2), 1188\u20131200 (2021)","journal-title":"IEEE Trans. Wirel. Comm."},{"doi-asserted-by":"crossref","unstructured":"Zeng, Q., Du, Y., Huang, K., Leung, K.K.: Energy-efficient radio resource allocation for federated edge learning. In: Proceedings of IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1\u20136, June 2020","key":"14_CR14","DOI":"10.1109\/ICCWorkshops49005.2020.9145118"},{"issue":"5","key":"14_CR15","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1109\/TWC.2011.012411.100787","volume":"10","author":"Y Xi","year":"2011","unstructured":"Xi, Y., Burr, A., Wei, J., Grace, D.: A general upper bound to evaluate packet error rate over quasi-static fading channels. IEEE Trans. Wirel. Commun. 10(5), 1373\u20131377 (2011)","journal-title":"IEEE Trans. Wirel. Commun."}],"container-title":["Lecture Notes in Computer Science","Smart Computing and Communication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-97774-0_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T08:10:16Z","timestamp":1647245416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-97774-0_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030977733","9783030977740"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-97774-0_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SmartCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Smart Computing and Communication","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New York, NY","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"smartc2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cloud-conf.net\/smartcom\/2021\/index.html","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":"165","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":"44","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":"3","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":"27% - 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","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":"10","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}