{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T22:54:02Z","timestamp":1781477642177,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819541485","type":"print"},{"value":"9789819541492","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-4149-2_18","type":"book-chapter","created":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T22:51:02Z","timestamp":1781477462000},"page":"259-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VHFed: A Two-Tier Vertical and\u00a0Horizontal Federated Learning Framework for\u00a0Enhanced Model Performance"],"prefix":"10.1007","author":[{"given":"Weiliang","family":"Ke","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zechen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyao","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"18_CR1","unstructured":"Liu, Y., et al.: Vertical Federated Learning. arXiv. https:\/\/arxiv.org\/abs\/2211.12814v2 (2022)"},{"key":"18_CR2","unstructured":"Hardy, S., et al.: Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017). arXiv preprint. https:\/\/arxiv.org\/abs\/1711.10677"},{"key":"18_CR3","unstructured":"Yang, S., Ren, B., Zhou, X., Liu, L.: Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-party Coordinator (2019). arXiv preprint. https:\/\/arxiv.org\/abs\/1911.09824"},{"key":"18_CR4","unstructured":"Feng, S., Yu, H.: Multi-participant multi-class vertical federated learning (2020). arXiv preprint. https:\/\/arxiv.org\/abs\/2001.11154"},{"issue":"1","key":"18_CR5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1109\/TNSE.2022.3206243","volume":"10","author":"Z Yang","year":"2023","unstructured":"Yang, Z., Zhou, M., Yu, H., Sinnott, R.O., Liu, H.: Efficient and secure federated learning with verifiable weighted average aggregation. IEEE Trans. Netw. Sci. Eng. 10(1), 205\u2013222 (2023)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"4","key":"18_CR6","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1109\/TNSE.2022.3168969","volume":"9","author":"L Tan","year":"2022","unstructured":"Tan, L., et al.: AdaFed: optimizing participation-aware federated learning with adaptive aggregation weights. IEEE Trans. Netw. Sci. Eng. 9(4), 2708\u20132720 (2022)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"18_CR7","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D.S., Khazaeni, Y.: Federated learning with matched averaging. ICLR 2020 (2020)"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Wang, Z., Fan, X., Qi, J., Wen, C., Wang, C., Yu, R.: Federated learning with fair averaging. IJCAI 2021, pp. 1615\u20131623 (2021)","DOI":"10.24963\/ijcai.2021\/223"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, L., Li, A., You, X., Cheng, H.: Efficient participant contribution evaluation for horizontal and vertical federated learning. ICDE 2022 (2022)","DOI":"10.1109\/ICDE53745.2022.00073"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Contribution-aware federated learning for smart healthcare. AAAI 2022, pp. 12396\u201312404 (2022)","DOI":"10.1609\/aaai.v36i11.21505"},{"key":"18_CR11","unstructured":"Pillutla, K., Malik, K., Mohamed, A., Rabbat, M., Sanjabi, M., Xiao, L.: Federated learning with partial model personalization. ICML 2022, pp. 17716\u201317758 (2022)"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Hierarchical personalized federated learning for user modeling. WWW 2021 (2021)","DOI":"10.1145\/3442381.3449926"},{"key":"18_CR13","unstructured":"Chen, H.-Y., Chao, W.-L.: On bridging generic and personalized federated learning for image classification. ICLR 2022 (2022)"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Gao, L., Fu, H., Li, L., Chen, Y., Xu, M., Xu, C.-Z.: FedDC: Federated learning with non-IID data via local drift decoupling and correction. CVPR 2022, pp. 10102\u201310111 (2022)","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Shen, Y., Zhou, Y., Yu, L.: CD$$^2$$-pFed: Cyclic distillation-guided channel decoupling for model personalization in federated learning. CVPR 2022 (2022)","DOI":"10.1109\/CVPR52688.2022.00980"},{"key":"18_CR16","unstructured":"Yi, L., et al.: FedSSA: semantic similarity-based aggregation for efficient model-heterogeneous personalized federated learning. IJCAI 2024, pp. 5371\u20135379 (2024)"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Xie, L., et al.: MH-pFLID: model heterogeneous personalized federated learning via injection and distillation for medical data analysis. ICML 2024 (2024)","DOI":"10.1007\/978-3-031-72117-5_50"},{"key":"18_CR18","unstructured":"Xu, H., Shen, D., Wang, M., Wang, B.: Adaptive group personalization for federated mutual transfer learning. ICML 2024 (2024)"},{"key":"18_CR19","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. MLSys 2020 (2020)"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, L., Shen, L., Ding, L., Tao, D., Duan, L.-Y.: Fine-tuning Global model via data-free knowledge distillation for non-IID federated learning. CVPR 2022, pp. 10164\u201310173 (2022)","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"18_CR21","unstructured":"Ma, Z., Lu, Y., Li, W., Yi, J., Cui, S.: PFedAtt: attention-based personalized federated learning on heterogeneous clients. ACML 2021, pp. 1253\u20131268 (2021)"},{"key":"18_CR22","first-page":"5972","volume":"34","author":"M Luo","year":"2021","unstructured":"Luo, M., Chen, F., Hu, D., Zhang, Y., Liang, J., Feng, J.: No fear of heterogeneity: classifier calibration for federated learning with non-iid data. NeurIPS 34, 5972\u20135984 (2021)","journal-title":"NeurIPS"},{"key":"18_CR23","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Ag\u00fcera y Arcas, B.: Communication-efficient learning of deep networks from decentralized data. AISTATS 2017, vol. 54, pp. 1273\u20131282 (2017)"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Kolesnikov, V., Kumaresan, R., Rosulek, M., Trieu, N.: Efficient batched oblivious PRF with applications to private set intersection. CCS 2016, pp. 818\u2013829 (2016)","DOI":"10.1145\/2976749.2978381"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4149-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T22:51:08Z","timestamp":1781477468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4149-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819541485","9789819541492"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4149-2_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2025.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}