{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:13:22Z","timestamp":1742998402029,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":12,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819771837"},{"type":"electronic","value":"9789819771844"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-981-97-7184-4_9","type":"book-chapter","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T06:41:16Z","timestamp":1724308876000},"page":"99-111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FGFL: Fine-Grained Federated Learning Based on\u00a0Neural Architecture Search for\u00a0Heterogeneous Clients"],"prefix":"10.1007","author":[{"given":"Weiqin","family":"Ying","sequence":"first","affiliation":[]},{"given":"Chixin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Zhe","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"9_CR1","unstructured":"Cai, H., Gan, C., Wang, T., Zhang, Z., Han, S.: Once-for-all: train one network and specialize it for efficient deployment. arXiv preprint arXiv:1908.09791 (2019)"},{"issue":"2","key":"9_CR2","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"9_CR3","unstructured":"Diao, E., Ding, J., Tarokh, V.: HeteroFL: computation and communication efficient federated learning for heterogeneous clients. arXiv preprint arXiv:2010.01264 (2020)"},{"key":"9_CR4","unstructured":"Dudziak, L., Laskaridis, S., Fernandez-Marques, J.: FedorAS: federated architecture search under system heterogeneity. arXiv preprint arXiv:2206.11239 (2022)"},{"key":"9_CR5","first-page":"12876","volume":"34","author":"S Horvath","year":"2021","unstructured":"Horvath, S., Laskaridis, S., Almeida, M., Leontiadis, I., Venieris, S., Lane, N.: FjORD: fair and accurate federated learning under heterogeneous targets with ordered dropout. Adv. Neural. Inf. Process. Syst. 34, 12876\u201312889 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR6","unstructured":"Kang, H., Cha, S., Shin, J., Lee, J., Kang, J.: NeFL: nested federated learning for heterogeneous clients. arXiv preprint arXiv:2308.07761 (2023)"},{"key":"9_CR7","unstructured":"Kim, M., Yu, S., Kim, S., Moon, S.M.: DepthFL: depthwise federated learning for heterogeneous clients. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"9_CR8","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Master\u2019s thesis, Department of Computer Science, University of Toronto (2009)"},{"key":"9_CR9","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":"9_CR10","unstructured":"Li, X., Jiang, M., Zhang, X., Kamp, M., Dou, Q.: FedBN: federated learning on non-IID features via local batch normalization. arXiv preprint arXiv:2102.07623 (2021)"},{"key":"9_CR11","unstructured":"Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameters sharing. In: International Conference on Machine Learning, pp. 4095\u20134104. PMLR (2018)"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Yu, J., Huang, T.S.: Universally slimmable networks and improved training techniques. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1803\u20131811 (2019)","DOI":"10.1109\/ICCV.2019.00189"}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-7184-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T06:41:54Z","timestamp":1724308914000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-7184-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819771837","9789819771844"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-7184-4_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xining","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"22 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iasei.org\/icsi2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}