{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:04:28Z","timestamp":1771697068485,"version":"3.50.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031442124","type":"print"},{"value":"9783031442131","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44213-1_43","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T05:01:41Z","timestamp":1695272501000},"page":"516-528","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["pFedLHNs: Personalized Federated Learning via\u00a0Local Hypernetworks"],"prefix":"10.1007","author":[{"given":"Liping","family":"Yi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaorong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyue","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoguang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"43_CR1","unstructured":"Acar, D.A.E., et al.: Federated learning based on dynamic regularization. In: Proceedings of ICLR, Virtual, Austria. OpenReview.net (2021)"},{"key":"43_CR2","unstructured":"Achituve, I., et al.: Personalized federated learning with gaussian processes. CoRR abs\/2106.15482 (2021)"},{"key":"43_CR3","doi-asserted-by":"crossref","unstructured":"Agrawal, S., et al.: Genetic CFL: optimization of hyper-parameters in clustered federated learning. CoRR abs\/2107.07233 (2021)","DOI":"10.1155\/2021\/7156420"},{"key":"43_CR4","unstructured":"Arivazhagan, M.G., et al.: Federated learning with personalization layers. CoRR abs\/1912.00818 (2019)"},{"key":"43_CR5","unstructured":"Chen, H., et al.: On bridging generic and personalized federated learning. CoRR abs\/2107.00778 (2021)"},{"key":"43_CR6","unstructured":"Chen, T., et al.: LAG: lazily aggregated gradient for communication-efficient distributed learning. In: Proceedings of NeurIPS 2018, pp. 5055\u20135065 (2018)"},{"key":"43_CR7","unstructured":"Collins, L., et al.: Exploiting shared representations for personalized federated learning. In: Proceedings of ICML, Virtual Event. vol. 139, pp. 2089\u20132099. PMLR (2021)"},{"key":"43_CR8","unstructured":"Diao, E.: Heterofl: Computation and communication efficient federated learning for heterogeneous clients. In: Proceedings of ICLR (2021)"},{"key":"43_CR9","unstructured":"Dinh, C.T., et al.: Personalized federated learning with moreau envelopes. In: Proceedings of NeurIPS virtual (2020)"},{"key":"43_CR10","unstructured":"Dinh, C.T., et al.: Fedu: A unified framework for federated multi-task learning with laplacian regularization. CoRR abs\/2102.07148 (2021)"},{"key":"43_CR11","unstructured":"Fallah, A., et al.: Personalized federated learning with theoretical guarantees: a model-agnostic meta-learning approach. In: Proceedings of NIPS, virtual (2020)"},{"key":"43_CR12","unstructured":"Ha, D., et al.: Hypernetworks. In: Proc. ICLR 2017, Toulon, France. OpenReview.net (2017)"},{"key":"43_CR13","unstructured":"Horv\u00e1th, S.: FjORD: Fair and accurate federated learning under heterogeneous targets with ordered dropout. In: Proceedings of NIPS (2021)"},{"key":"43_CR14","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: Personalized cross-silo federated learning on non-iid data. In: Proceedings of AAAI. pp. 7865\u20137873. AAAI Press (2021)","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"43_CR15","unstructured":"Krizhevsky, A., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"43_CR16","doi-asserted-by":"crossref","unstructured":"Kulkarni, V., et al.: Survey of personalization techniques for federated learning. CoRR abs\/2003.08673 (2020)","DOI":"10.1109\/WorldS450073.2020.9210355"},{"issue":"11","key":"43_CR17","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"43_CR18","doi-asserted-by":"crossref","unstructured":"Li, Q., et al.: Federated learning on non-iid data silos: An experimental study. CoRR abs\/2102.02079 (2021)","DOI":"10.1145\/3447548.3467254"},{"key":"43_CR19","unstructured":"Li, T., et al.: Federated optimization in heterogeneous networks. In: Proceedings MLSys, Austin, TX, USA. mlsys.org (2020)"},{"key":"43_CR20","unstructured":"Li, T., et al.: Ditto: Fair and robust federated learning through personalization. In: Proceedings of ICML, Virtual Event. vol. 139, pp. 6357\u20136368. PMLR (2021)"},{"key":"43_CR21","unstructured":"Liang, P.P., et al.: Think locally, act globally: Federated learning with local and global representations. CoRR abs\/2001.01523 (2020)"},{"issue":"7","key":"43_CR22","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1109\/LWC.2021.3074605","volume":"10","author":"S Liu","year":"2021","unstructured":"Liu, S., et al.: Adaptive network pruning for wireless federated learning. IEEE Wirel. Commun. Lett. 10(7), 1572\u20131576 (2021)","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"43_CR23","unstructured":"Mansour, Y., et al.: Three approaches for personalization with applications to federated learning. CoRR abs\/2002.10619 (2020)"},{"key":"43_CR24","unstructured":"McMahan, B., et al.: Communication-efficient learning of deep networks from decentralized data. In: Proceeidngs of AISTATS, Fort Lauderdale, FL, USA. vol. 54, pp. 1273\u20131282. PMLR (2017)"},{"key":"43_CR25","doi-asserted-by":"crossref","unstructured":"Ouyang, X., et al.: Clusterfl: a similarity-aware federated learning system for human activity recognition. In: Proceedings of MobiSys, USA, pp. 54\u201366. ACM (2021)","DOI":"10.1145\/3458864.3467681"},{"key":"43_CR26","unstructured":"Reisizadeh, A., et al.: Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization. In: Proceedings of AISTATS. vol. 108, pp. 2021\u20132031. PMLR, Online (2020)"},{"key":"43_CR27","unstructured":"Sai, et al.: SCAFFOLD: stochastic controlled averaging for federated learning. In: Proceedings ICML, Virtual Event. vol. 119, pp. 5132\u20135143. PMLR (2020)"},{"key":"43_CR28","unstructured":"Shamsian, A., et al.: Personalized federated learning using hypernetworks. In: Proceedings of ICML, Virtual Event. vol. 139, pp. 9489\u20139502. PMLR (2021)"},{"key":"43_CR29","doi-asserted-by":"crossref","unstructured":"Shi, X., et al.: FFedCL: Fair Federated Learning with Contrastive Learning. In: Proceedings of ICASSP (2023)","DOI":"10.1109\/ICASSP49357.2023.10095453"},{"key":"43_CR30","unstructured":"Smith, V., et al.: Federated multi-task learning. In: Proceedings of NIPS, Long Beach, CA, USA, pp. 4424\u20134434 (2017)"},{"key":"43_CR31","unstructured":"Sun, B., et al.: Partialfed: Cross-domain personalized federated learning via partial initialization. In: Proceedings of NIPS, Virtual Event (2021)"},{"key":"43_CR32","unstructured":"Sun, J., et al.: Communication-efficient distributed learning via lazily aggregated quantized gradients. In: Proceedings of NeurIPS, Canada, pp. 3365\u20133375 (2019)"},{"key":"43_CR33","unstructured":"Wang, K., et al.: Federated evaluation of on-device personalization. CoRR abs\/1910.10252 (2019)"},{"key":"43_CR34","doi-asserted-by":"crossref","unstructured":"Yang, T., et al.: Designing energy-efficient convolutional neural networks using energy-aware pruning. In: Proceedings of CVPR, USA, pp. 6071\u20136079. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.643"},{"key":"43_CR35","doi-asserted-by":"crossref","unstructured":"Yi, L., et al.: SU-Net: an efficient encoder-decoder model of federated learning for brain tumor segmentation. In: Proceedings of ICANN (2020)","DOI":"10.1007\/978-3-030-61609-0_60"},{"key":"43_CR36","unstructured":"Yi, L., et al.: QSFL: A two-level uplink communication optimization framework for federated learning. In: Proceedings of ICML. vol. 162, pp. 25501\u201325513. PMLR (2022)"},{"key":"43_CR37","doi-asserted-by":"crossref","unstructured":"Yi, L., et al.: FedRRA: reputation-aware robust federated learning against poisoning attacks. In: Proceedings of IJCNN (2023)","DOI":"10.1109\/IJCNN54540.2023.10191556"},{"key":"43_CR38","unstructured":"Zhang, M., et al.: Personalized federated learning with first order model optimization. In: Proceedings of ICLR Virtual Event, Austria. OpenReview.net (2021)"},{"key":"43_CR39","unstructured":"Zhao, Y., et al.: Federated learning with non-iid data. CoRR abs\/1806.00582 (2018)"},{"key":"43_CR40","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","volume":"465","author":"H Zhu","year":"2021","unstructured":"Zhu, H., et al.: Federated learning on non-iid data: a survey. Neurocomputing 465, 371\u2013390 (2021)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44213-1_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:23:09Z","timestamp":1695277389000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44213-1_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442124","9783031442131"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44213-1_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted  : 9 Abstract","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)"}}]}}