{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:37:01Z","timestamp":1775090221063,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030864859","type":"print"},{"value":"9783030864866","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86486-6_36","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T15:25:48Z","timestamp":1631201148000},"page":"587-602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["FedPHP: Federated Personalization with\u00a0Inherited Private Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9417-7971","authenticated-orcid":false,"given":"Xin-Chun","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9303-2519","authenticated-orcid":false,"given":"De-Chuan","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Yunfeng","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Bingshuai","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shaoming","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"36_CR1","unstructured":"Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S.: Federated learning with personalization layers. CoRR abs\/1912.00818 (2019)"},{"key":"36_CR2","unstructured":"Caldas, S., et al.: LEAF: A benchmark for federated settings. CoRR abs\/1812.01097 (2018)"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.J.: A kernelmethod for the two-sample-problem. In: Advances in Neural Information ProcessingSystems 19, pp. 513\u2013520 (2006)","DOI":"10.7551\/mitpress\/7503.003.0069"},{"key":"36_CR4","unstructured":"Hamer, J., Mohri, M., Suresh, A.T.: FedBoost: a communication-efficient algorithm for federated learning. In: Proceedings of the 37th International Conference on Machine Learning, pp. 3973\u20133983 (2020)"},{"key":"36_CR5","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. CoRR abs\/1503.02531 (2015)"},{"key":"36_CR6","unstructured":"Kairouz, P., et al.: Advances and open problems in federated learning. CoRR abs\/1912.04977 (2019)"},{"key":"36_CR7","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S.J., Stich, S.U., Suresh, A.T.: SCAFFOLD: stochastic controlled averaging for federated learning. In: Proceedings of the 37th International Conference on Machine Learning, pp. 5132\u20135143 (2020)"},{"key":"36_CR8","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images (2012)"},{"key":"36_CR9","unstructured":"Kulkarni, V., Kulkarni, M., Pant, A.: Survey of personalization techniques for federated learning. CoRR abs\/2003.08673 (2020)"},{"key":"36_CR10","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: 5th International Conference on Learning Representations (2017)"},{"key":"36_CR11","unstructured":"Li, D., Wang, J.: FedMD: Heterogenous federated learning via model distillation. CoRR abs\/1910.03581 (2019)"},{"key":"36_CR12","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. In: Proceedings of Machine Learning and Systems (2020)"},{"key":"36_CR13","unstructured":"Li, X., Grandvalet, Y., Davoine, F.: Explicit inductive bias for transfer learning with convolutional networks. In: Proceedings of the 35th International Conference on Machine Learning, vol. 80, pp. 2830\u20132839 (2018)"},{"key":"36_CR14","unstructured":"Liang, P.P., Liu, T., Liu, Z., Salakhutdinov, R., Morency, L.: Think locally, act globally: Federated learning with local and global representations. CoRR abs\/2001.01523 (2020)"},{"key":"36_CR15","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: Proceedings of the 32nd International Conference on Machine Learning, vol. 37, pp. 97\u2013105 (2015)"},{"key":"36_CR16","unstructured":"Mansour, Y., Mohri, M., Ro, J., Suresh, A.T.: Three approaches for personalization with applications to federated learning. CoRR abs\/2002.10619 (2020)"},{"key":"36_CR17","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017)"},{"issue":"10","key":"36_CR18","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"36_CR19","unstructured":"Peterson, D., Kanani, P., Marathe, V.J.: Private federated learning with domain adaptation. CoRR abs\/1912.06733 (2019)"},{"key":"36_CR20","unstructured":"Shen, T., et al.: Federated mutual learning. CoRR abs\/2006.16765 (2020)"},{"key":"36_CR21","unstructured":"Shoham, N., et al.: Overcoming forgetting in federated learning on non-iid data. CoRR abs\/1910.07796 (2019)"},{"key":"36_CR22","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: 5th International Conference on Learning Representations (2017)"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM TIST 10(2), 12:1\u201312:19 (2019)","DOI":"10.1145\/3298981"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Yao, X., Huang, C., Sun, L.: Two-stream federated learning: reduce the communication costs. In: IEEE Visual Communications and Image Processing, pp. 1\u20134 (2018)","DOI":"10.1109\/VCIP.2018.8698609"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Yao, X., Huang, T., Wu, C., Zhang, R., Sun, L.: Towards faster and better federated learning: a feature fusion approach. In: IEEE International Conference on Image Processing, pp. 175\u2013179 (2019)","DOI":"10.1109\/ICIP.2019.8803001"},{"key":"36_CR26","unstructured":"Yu, T., Bagdasaryan, E., Shmatikov, V.: Salvaging federated learning by local adaptation. CoRR abs\/2002.04758 (2020)"},{"key":"36_CR27","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-iid data. CoRR abs\/1806.00582 (2018)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86486-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:05:39Z","timestamp":1757369139000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86486-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030864859","9783030864866"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86486-6_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 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":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"869","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":"210","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":"24% - 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":"3-4","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":"3-9","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":"The 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)"}}]}}