{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:29:55Z","timestamp":1775521795497,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"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":["Peer-to-Peer Netw. Appl."],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s12083-021-01254-8","type":"journal-article","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T09:03:32Z","timestamp":1642151012000},"page":"1139-1151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Energy-efficient client selection in federated learning with heterogeneous data on edge"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0198-676X","authenticated-orcid":false,"given":"Jianxin","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yanhao","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Chi Harold","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"1254_CR1","unstructured":"Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., He, B.: A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection pp. 1\u201341 (2019). http:\/\/arxiv.org\/abs\/1907.09693"},{"key":"1254_CR2","unstructured":"Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konecny J, Mazzocchi S, McMahan HB (2019)\u00a0Others: Towards federated learning at scale: System design. arXiv preprint\u00a0arXiv:1902.01046"},{"key":"1254_CR3","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R et\u00a0al (2019)\u00a0Advances and open problems in federated learning. arXiv preprint\u00a0arXiv:1912.04977"},{"key":"1254_CR4","doi-asserted-by":"crossref","unstructured":"Saputra YM, Hoang DT, Nguyen DN, Dutkiewicz E, Mueck MD, Srikanteswara S (2019)\u00a0Energy demand prediction with federated learning for electric vehicle networks. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136. IEEE","DOI":"10.1109\/GLOBECOM38437.2019.9013587"},{"issue":"5","key":"1254_CR5","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/MNET.2019.1800286","volume":"33","author":"X Wang","year":"2019","unstructured":"Wang X, Han Y, Wang C, Zhao Q, Chen X, Chen M (2019) In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw 33(5):156\u2013165","journal-title":"IEEE Netw"},{"key":"1254_CR6","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ijmedinf.2018.01.007","volume":"112","author":"TS Brisimi","year":"2018","unstructured":"Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W (2018) Federated learning of predictive models from federated electronic health records. Int J Med Inform 112:59\u201367","journal-title":"Int J Med Inform"},{"key":"1254_CR7","unstructured":"Hard A, Rao K, Mathews R, Ramaswamy S, Beaufays F, Augenstein S, Eichner H, Kiddon C, Ramage D (2018)\u00a0Federated learning for mobile keyboard prediction. arXiv preprint\u00a0arXiv:1811.03604"},{"key":"1254_CR8","unstructured":"Brendan McMahan H, Moore E, Ramage D, Hampson S, Ag\u00fcera y Arcas B (2017)\u00a0Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. AISTATS 2017 54"},{"key":"1254_CR9","unstructured":"Caldas S, Duddu SMK, Wu P, Li T, Kone\u010dn\u00fd J, McMahan HB, Smith V, Talwalkar A (2018)\u00a0LEAF: A Benchmark for Federated Settings (NeurIPS) 1\u20139"},{"key":"1254_CR10","doi-asserted-by":"crossref","unstructured":"Li M, Andersen DG, Park JW, Smola AJ, Ahmed A, Josifovski V, Long J, Shekita EJ, Su BY (2014)\u00a0Scaling distributed machine learning with the parameter server. In: Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation. OSDI 2014","DOI":"10.1145\/2640087.2644155"},{"key":"1254_CR11","unstructured":"Sergeev A, Balso MD (2017)\u00a0Horovod: fast and easy distributed deep learning in TensorFlow (September)"},{"key":"1254_CR12","doi-asserted-by":"publisher","unstructured":"Wang J, Sahu AK, Yang Z, Joshi G, Kar S (2019)\u00a0MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling. 2019 6th Indian Control Conference, ICC 2019 - Proceedings pp. 299\u2013300. https:\/\/doi.org\/10.1109\/ICC47138.2019.9123209","DOI":"10.1109\/ICC47138.2019.9123209"},{"issue":"3","key":"1254_CR13","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: Challenges, methods, and future directions. IEEE Signal Process Mag 37(3):50\u201360","journal-title":"IEEE Signal Process Mag"},{"issue":"3","key":"1254_CR14","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","volume":"22","author":"WYB Lim","year":"2020","unstructured":"Lim WYB, Luong NC, Hoang DT, Jiao Y, Liang YC, Yang Q, Niyato D, Miao C (2020) Federated learning in mobile edge networks: A comprehensive survey. IEEE Commun Surv Tutorials 22(3):2031\u20132063","journal-title":"IEEE Commun Surv Tutorials"},{"key":"1254_CR15","doi-asserted-by":"crossref","unstructured":"Nishio T, Yonetani R (2019)\u00a0Client selection for federated learning with heterogeneous resources in mobile edge. In: ICC 2019-2019 IEEE International Conference on Communications (ICC), pp. 1\u20137. IEEE","DOI":"10.1109\/ICC.2019.8761315"},{"key":"1254_CR16","unstructured":"Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018)\u00a0Federated learning with non-iid data. arXiv preprint\u00a0arXiv:1806.00582"},{"key":"1254_CR17","unstructured":"Li X, Huang K, Yang W, Wang S, Zhang Z (2019) On the Convergence of FedAvg on Non-IID Data\u00a01\u201326. http:\/\/arxiv.org\/abs\/1907.02189"},{"issue":"1","key":"1254_CR18","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/TPDS.2020.3009406","volume":"32","author":"M Duan","year":"2021","unstructured":"Duan M, Liu D, Chen X, Liu R, Tan Y, Liang L (2021) Self-Balancing Federated Learning with Global Imbalanced Data in Mobile Systems. IEEE Trans Parallel Distrib Syst 32(1):59\u201371. https:\/\/doi.org\/10.1109\/TPDS.2020.3009406","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"1254_CR19","doi-asserted-by":"crossref","unstructured":"Yoshida N, Nishio T, Morikura M, Yamamoto K, Yonetani R (2020)\u00a0Hybrid-fl for wireless networks: Cooperative learning mechanism using non-iid data. In: ICC 2020-2020 IEEE International Conference on Communications (ICC), pp. 1\u20137. IEEE","DOI":"10.1109\/ICC40277.2020.9149323"},{"key":"1254_CR20","unstructured":"Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2018)\u00a0Federated Optimization in Heterogeneous Networks. http:\/\/arxiv.org\/abs\/1812.06127"},{"key":"1254_CR21","unstructured":"Li C, Li R, Wang H, Li Y, Zhou P, Guo S, Li K (2019)\u00a0Gradient scheduling with global momentum for non-iid data distributed asynchronous training. arXiv preprint\u00a0arXiv:1902.07848"},{"key":"1254_CR22","doi-asserted-by":"crossref","unstructured":"Wang H, Kaplan Z, Niu D, Li B (2020)\u00a0Optimizing federated learning on non-iid data with reinforcement learning. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 1698\u20131707. IEEE","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"1254_CR23","unstructured":"Mohri M, Sivek G, Suresh AT (2019)\u00a0Agnostic federated learning. In: International Conference on Machine Learning, pp. 4615\u20134625. PMLR"},{"key":"1254_CR24","unstructured":"Cho YJ, Wang J, Joshi G (2020)\u00a0Client selection in federated learning: Convergence analysis and power-of-choice selection strategies. arXiv preprint arXiv:2010.01243"},{"key":"1254_CR25","unstructured":"Tang M, Ning X, Wang Y, Wang Y, Chen Y (2021)\u00a0Fedgp: Correlation-based active client selection for heterogeneous federated learning. arXiv preprint arXiv:2103.13822"},{"issue":"5","key":"1254_CR26","doi-asserted-by":"publisher","first-page":"3394","DOI":"10.1109\/JIOT.2020.3022534","volume":"8","author":"VD Nguyen","year":"2020","unstructured":"Nguyen VD, Sharma SK, Vu TX, Chatzinotas S, Ottersten B (2020) Efficient federated learning algorithm for resource allocation in wireless iot networks. IEEE Internet Things J 8(5):3394\u20133409","journal-title":"IEEE Internet Things J"},{"issue":"1","key":"1254_CR27","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1109\/TNET.2020.3035770","volume":"29","author":"CT Dinh","year":"2021","unstructured":"Dinh CT, Tran NH, Nguyen MNH, Hong CS, Bao W, Zomaya AY, Gramoli V (2021) Federated learning over wireless networks: Convergence analysis and resource allocation. IEEE\/ACM Trans Networking 29(1):398\u2013409. https:\/\/doi.org\/10.1109\/TNET.2020.3035770","journal-title":"IEEE\/ACM Trans Networking"},{"key":"1254_CR28","doi-asserted-by":"publisher","unstructured":"Wang S, Chen M, Saad W, Yin C (2020)\u00a0Federated learning for energy-efficient task computing in wireless networks. In: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICC40277.2020.9148625","DOI":"10.1109\/ICC40277.2020.9148625"},{"key":"1254_CR29","unstructured":"Kone\u010dn\u1ef3 J, McMahan HB, Yu FX, Richt\u00e1rik P, Suresh AT, Bacon D (2016)\u00a0Federated learning: Strategies for improving communication efficiency. arXiv preprint\u00a0arXiv:1610.05492"},{"key":"1254_CR30","doi-asserted-by":"crossref","unstructured":"Li L, Shi D, Hou R, Li H, Pan M, Han Z (2021)\u00a0To talk or to work: Flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp. 1\u201310. IEEE","DOI":"10.1109\/INFOCOM42981.2021.9488839"},{"key":"1254_CR31","unstructured":"Hsieh K, Harlap A, Vijaykumar N, Konomis D, Ganger GR, Gibbons PB, Mutlu O (2017)\u00a0Gaia: Geo-distributed machine learning approaching LAN speeds. Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017 pp. 629\u2013647"},{"key":"1254_CR32","doi-asserted-by":"crossref","unstructured":"Sun Y, Zhou S, G\u00fcnd\u00fcz D (2020)\u00a0Energy-aware analog aggregation for federated learning with redundant data. In: ICC 2020-2020 IEEE International Conference on Communications (ICC), pp. 1\u20137. IEEE","DOI":"10.1109\/ICC40277.2020.9148853"},{"key":"1254_CR33","doi-asserted-by":"crossref","unstructured":"Zhang J, Simeone O (2020)\u00a0Lagc: Lazily aggregated gradient coding for straggler-tolerant and communication-efficient distributed learning. IEEE transactions on neural networks and learning systems","DOI":"10.1109\/TNNLS.2020.2979762"},{"key":"1254_CR34","doi-asserted-by":"crossref","unstructured":"Singh N, Data D, George J, Diggavi S (2020)\u00a0Sparq-sgd: Event-triggered and compressed communication in decentralized optimization. In: 2020 59th IEEE Conference on Decision and Control (CDC), pp. 3449\u20133456. IEEE","DOI":"10.1109\/CDC42340.2020.9303828"},{"key":"1254_CR35","unstructured":"Amirhosein\u00a0Bodaghi SG (2017)\u00a0Dynamics of Instagram Users. https:\/\/zenodo.org\/record\/823283. [Online; accessed 23-July-2021]"},{"key":"1254_CR36","unstructured":"Kingma DP, Ba J (2014)\u00a0Adam: A method for stochastic optimization. arXiv preprint\u00a0arXiv:1412.6980"}],"container-title":["Peer-to-Peer Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-021-01254-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12083-021-01254-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-021-01254-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T08:20:23Z","timestamp":1647418823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12083-021-01254-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,14]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["1254"],"URL":"https:\/\/doi.org\/10.1007\/s12083-021-01254-8","relation":{},"ISSN":["1936-6442","1936-6450"],"issn-type":[{"value":"1936-6442","type":"print"},{"value":"1936-6450","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,14]]},"assertion":[{"value":"27 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}