{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:13:46Z","timestamp":1743146026624,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030953904"},{"type":"electronic","value":"9783030953911"}],"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\/978-3-030-95391-1_27","type":"book-chapter","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T09:04:54Z","timestamp":1645520694000},"page":"432-446","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Robust Multi-model Personalized Federated Learning via\u00a0Model Distillation"],"prefix":"10.1007","author":[{"given":"Adil","family":"Muhammad","sequence":"first","affiliation":[]},{"given":"Kai","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Bincai","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","unstructured":"Voigt, P., von dem Bussche, A.: Introduction and \u2018checklist\u2019. The EU General Data Protection Regulation (GDPR). https:\/\/doi.org\/10.1007\/978-3-319-57959-7","DOI":"10.1007\/978-3-319-57959-7"},{"key":"27_CR2","doi-asserted-by":"publisher","unstructured":"Annas, G.J.: HIPAA regulations - a new era of medical-record privacy? N. Engl. J. Med. 348, 1486 (2003). https:\/\/doi.org\/10.1056\/nejmlim035027. ISSN 0028-4793","DOI":"10.1056\/nejmlim035027"},{"key":"27_CR3","doi-asserted-by":"publisher","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 (2016). https:\/\/doi.org\/10.1109\/ALLERTON.2015.7447103. ISBN 9781509018239","DOI":"10.1109\/ALLERTON.2015.7447103"},{"key":"27_CR4","doi-asserted-by":"publisher","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10, 1\u20139 (2019). https:\/\/doi.org\/10.1145\/3298981, arXiv: 1902.04885. ISSN 21576912","DOI":"10.1145\/3298981"},{"key":"27_CR5","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. arXiv:1602.05629 [cs.LG] (2017)"},{"key":"27_CR6","doi-asserted-by":"publisher","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50\u201360 (2020). https:\/\/doi.org\/10.1109\/MSP.2020.2975749, arXiv:1908.07873. ISSN 15580792","DOI":"10.1109\/MSP.2020.2975749"},{"key":"27_CR7","doi-asserted-by":"publisher","unstructured":"Nishio, T., Yonetani, R.: Client selection for federated learning with heterogeneous resources in mobile edge. In: IEEE International Conference on Communications (2019). https:\/\/doi.org\/10.1109\/ICC.2019.8761315, arXiv:1804.08333. ISBN 9781538680889","DOI":"10.1109\/ICC.2019.8761315"},{"key":"27_CR8","unstructured":"Sahu, A.K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., Smith, V.: On the convergence of federated optimization in heterogeneous networks. arXiv:1812.06127 [cs, stat] (2018)"},{"key":"27_CR9","unstructured":"Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.: Federated multi-task learning. In: Advances in Neural Information Processing Systems (2017)"},{"key":"27_CR10","doi-asserted-by":"publisher","unstructured":"Wu, Q., He, K., Chen, X.: Personalized federated learning for intelligent IoT applications: a cloud-edge based framework. IEEE Open J. Comput. Soc. 1, 35\u201344 (2020). https:\/\/doi.org\/10.1109\/OJCS.2020.2993259, arXiv:2002.10671. ISSN 15581756","DOI":"10.1109\/OJCS.2020.2993259"},{"key":"27_CR11","unstructured":"Li, D., Wang, J.: FedMD: heterogenous federated learning via model distillation. arXiv:1910.03581 (2019)"},{"key":"27_CR12","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv:1503.02531, March 2015"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., Niyato, D.: Federated learning for 6G communications: challenges, methods, and future directions. CoRR arXiv:2006.02931 (2020)","DOI":"10.23919\/JCC.2020.09.009"},{"key":"27_CR14","unstructured":"Kairouz, P., et al.: Advances and open problems in federated learning. CoRR arXiv:1912.04977 (2019)"},{"issue":"3","key":"27_CR15","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","volume":"22","author":"WYB Lim","year":"2020","unstructured":"Lim, W.Y.B., et al.: Federated learning in mobile edge networks: a comprehensive survey. IEEE Commun. Surv. Tutor. 22(3), 2031\u20132063 (2020). https:\/\/doi.org\/10.1109\/COMST.2020.2986024","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"27_CR16","unstructured":"Lin, T., Stich, S.U., Patel, K.K., Jaggi, M.: Don\u2019t use large mini-batches, use local SGD. arXiv:1808.07217 [cs.LG] (2020)"},{"key":"27_CR17","unstructured":"Sattler, F., M\u00fcller, K.-R., Samek, W.: Clustered federated learning: model-agnostic distributed multi-task optimization under privacy constraints. arXiv:1910.01991 [cs.LG] (2019)"},{"key":"27_CR18","unstructured":"Hanzely, F., Richt\u00e1rik, P.: Federated learning of a mixture of global and local models. arXiv:2002.05516 [cs.LG] (2021)"},{"key":"27_CR19","unstructured":"Diao, E., Ding, J., Tarokh, V.: HeteroFL: Computation and communication efficient federated learning for heterogeneous clients. arXiv:2010.01264 [cs.LG] (2021)"},{"issue":"2","key":"27_CR20","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/TNSE.2020.2996612","volume":"8","author":"H Yang","year":"2021","unstructured":"Yang, H., He, H., Zhang, W., Cao, X.: FedSteg: a federated transfer learning framework for secure image steganalysis. IEEE Trans. Netw. Sci. Eng. 8(2), 1084\u20131094 (2021). https:\/\/doi.org\/10.1109\/TNSE.2020.2996612","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"27_CR21","unstructured":"Chen, Y., et al.: FedHealth: a federated transfer learning framework for wearable healthcare. arXiv: 1907.09173 [cs.LG] (2021)"},{"key":"27_CR22","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. arXiv:1412.6550, December 2014"},{"key":"27_CR23","unstructured":"Chang, H., Shejwalkar, V., Shokri, R., Houmansadr, A.: Cronus: robust and heterogeneous collaborative learning with black-box knowledge transfer. arXiv:1912.11279, December 2019"},{"key":"27_CR24","unstructured":"Hashem, S., Schmeiser, B.: Approximating a function and its derivatives using MSE-optimal linear combinations of trained feedforward neural networks. In: Proceedings of the Joint Conference on Neural Networks, pp. 617\u2013620 (1993)"},{"issue":"8","key":"27_CR25","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1109\/TNNLS.2012.2200299","volume":"23","author":"SE Yuksel","year":"2012","unstructured":"Yuksel, S.E., Wilson, J.N., Gader, P.D.: Twenty years of mixture of experts. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1177\u20131193 (2012). https:\/\/doi.org\/10.1109\/TNNLS.2012.2200299","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"27_CR26","unstructured":"Schapire, R.E.: A brief introduction to boosting. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI\u201999, San Francisco, CA, USA, pp. 1401\u20131406. Morgan Kaufmann Publishers Inc. (1999)"},{"key":"27_CR27","unstructured":"Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation and active learning. In: Proceedings of the 7th International Conference on Neural Information Processing Systems, NIPS\u201994, Cambridge, MA, USA, pp. 231\u2013238. MIT Press (1994)"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95391-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T09:08:56Z","timestamp":1645520936000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95391-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030953904","9783030953911"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95391-1_27","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":"23 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ica3pp2021\/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":"403","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":"145","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":"36% - 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":"3.12","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":"2.27","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)"}}]}}