{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:04:43Z","timestamp":1773151483817,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030351656","type":"print"},{"value":"9783030351663","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-35166-3_34","type":"book-chapter","created":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T10:01:27Z","timestamp":1573898487000},"page":"477-491","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Towards Effective Device-Aware Federated Learning"],"prefix":"10.1007","author":[{"given":"Vito Walter","family":"Anelli","sequence":"first","affiliation":[]},{"given":"Yashar","family":"Deldjoo","sequence":"additional","affiliation":[]},{"given":"Tommaso","family":"Di Noia","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Ferrara","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,12]]},"reference":[{"key":"34_CR1","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. arXiv preprint \narXiv:1807.00459\n\n (2018)"},{"key":"34_CR2","unstructured":"Bonawitz, K., et al.: Towards federated learning at scale: system design. CoRR abs\/1902.01046 (2019). \nhttp:\/\/arxiv.org\/abs\/1902.01046"},{"key":"34_CR3","unstructured":"Caldas, S., et al.: Leaf: a benchmark for federated settings. arXiv preprint \narXiv:1812.01097\n\n (2018)"},{"key":"34_CR4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.5802\/aif.53","volume":"5","author":"G Choquet","year":"1954","unstructured":"Choquet, G.: Theory of capacities. Annales de l\u2019Institut Fourier 5, 131\u2013295 (1954). \nhttps:\/\/doi.org\/10.5802\/aif.53","journal-title":"Annales de l\u2019Institut Fourier"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Cohen, G., Afshar, S., Tapson, J., van Schaik, A.: EMNIST: extending MNIST to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921\u20132926. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966217"},{"issue":"2","key":"34_CR6","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.ipm.2011.07.001","volume":"48","author":"C Costa Pereira da","year":"2012","unstructured":"da Costa Pereira, C., Dragoni, M., Pasi, G.: Multidimensional relevance: prioritized aggregation in a personalized information retrieval setting. Inf. Process. Manag. 48(2), 340\u2013357 (2012). \nhttps:\/\/doi.org\/10.1016\/j.ipm.2011.07.001","journal-title":"Inf. Process. Manag."},{"issue":"3","key":"34_CR7","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/0377-2217(95)00176-X","volume":"89","author":"M Grabisch","year":"1996","unstructured":"Grabisch, M.: The application of fuzzy integrals in multicriteria decision making. Eur. J. Oper. Res. 89(3), 445\u2013456 (1996). \nhttps:\/\/doi.org\/10.1016\/0377-2217(95)00176-X\n\n. \nhttp:\/\/www.sciencedirect.com\/science\/article\/pii\/037722179500176X","journal-title":"Eur. J. Oper. Res."},{"key":"34_CR8","unstructured":"Grabisch, M., Roubens, M.: Application of the Choquet integral in multicriteria decision making. In: Fuzzy Measures and Integrals, pp. 348\u2013374 (2000)"},{"key":"34_CR9","unstructured":"Konecn\u00fd, J., McMahan, B., Ramage, D.: Federated optimization: distributed optimization beyond the datacenter. CoRR abs\/1511.03575 (2015). \nhttp:\/\/arxiv.org\/abs\/1511.03575"},{"key":"34_CR10","unstructured":"Konecn\u00fd, J., McMahan, H.B., Ramage, D., Richt\u00e1rik, P.: Federated optimization: distributed machine learning for on-device intelligence. CoRR abs\/1610.02527 (2016). \nhttp:\/\/arxiv.org\/abs\/1610.02527"},{"key":"34_CR11","unstructured":"Konecn\u00fd, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. CoRR abs\/1610.05492 (2016). \nhttp:\/\/arxiv.org\/abs\/1610.05492"},{"issue":"11","key":"34_CR12","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). \nhttps:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"34_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.fss.2016.12.018","volume":"324","author":"S Marrara","year":"2017","unstructured":"Marrara, S., Pasi, G., Viviani, M.: Aggregation operators in information retrieval. Fuzzy Sets Syst. 324, 3\u201319 (2017). \nhttps:\/\/doi.org\/10.1016\/j.fss.2016.12.018","journal-title":"Fuzzy Sets Syst."},{"key":"34_CR14","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., 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, Fort Lauderdale, FL, USA, 20\u201322 April 2017, pp. 1273\u20131282 (2017). \nhttp:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"issue":"5","key":"34_CR15","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MITP.2012.93","volume":"14","author":"KW Miller","year":"2012","unstructured":"Miller, K.W., Voas, J.M., Hurlburt, G.F.: BYOD: security and privacyconsiderations. IT Prof. 14(5), 53\u201355 (2012). \nhttps:\/\/doi.org\/10.1109\/MITP.2012.93","journal-title":"IT Prof."},{"key":"34_CR16","unstructured":"Sahu, A.K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., Smith, V.: On the convergence of federated optimization in heterogeneous networks. arXiv preprint \narXiv:1812.06127\n\n (2018)"},{"issue":"1","key":"34_CR17","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1109\/21.87068","volume":"18","author":"RR Yager","year":"1988","unstructured":"Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18(1), 183\u2013190 (1988). \nhttps:\/\/doi.org\/10.1109\/21.87068","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"1","key":"34_CR18","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1002\/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.0.CO;2-Z","volume":"11","author":"RR Yager","year":"1996","unstructured":"Yager, R.R.: Quantifier guided aggregation using OWA operators. Int. J. Intell. Syst. 11(1), 49\u201373 (1996)","journal-title":"Int. J. Intell. Syst."},{"key":"34_CR19","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data. CoRR abs\/1806.00582 (2018). \nhttp:\/\/arxiv.org\/abs\/1806.00582"}],"container-title":["Lecture Notes in Computer Science","AI*IA 2019 \u2013 Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-35166-3_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T10:05:58Z","timestamp":1573898758000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-35166-3_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030351656","9783030351663"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-35166-3_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"12 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI*IA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of the Italian Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rende","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiia2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aiia2019.mat.unical.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}