{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T18:09:35Z","timestamp":1783620575785,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSFC","award":["62472460"],"award-info":[{"award-number":["62472460"]}]},{"name":"NSFC","award":["62102460"],"award-info":[{"award-number":["62102460"]}]},{"name":"NSFC","award":["2024A1515010161"],"award-info":[{"award-number":["2024A1515010161"]}]},{"name":"NSFC","award":["2023A1515012982"],"award-info":[{"award-number":["2023A1515012982"]}]},{"name":"NSFC","award":["RG91\/22"],"award-info":[{"award-number":["RG91\/22"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62472460"],"award-info":[{"award-number":["62472460"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["62102460"],"award-info":[{"award-number":["62102460"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2024A1515010161"],"award-info":[{"award-number":["2024A1515010161"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2023A1515012982"],"award-info":[{"award-number":["2023A1515012982"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["RG91\/22"],"award-info":[{"award-number":["RG91\/22"]}]},{"name":"Young Outstanding Award under the Zhujiang Talent Plan of Guangdong Province","award":["62472460"],"award-info":[{"award-number":["62472460"]}]},{"name":"Young Outstanding Award under the Zhujiang Talent Plan of Guangdong Province","award":["62102460"],"award-info":[{"award-number":["62102460"]}]},{"name":"Young Outstanding Award under the Zhujiang Talent Plan of Guangdong Province","award":["2024A1515010161"],"award-info":[{"award-number":["2024A1515010161"]}]},{"name":"Young Outstanding Award under the Zhujiang Talent Plan of Guangdong Province","award":["2023A1515012982"],"award-info":[{"award-number":["2023A1515012982"]}]},{"name":"Young Outstanding Award under the Zhujiang Talent Plan of Guangdong Province","award":["RG91\/22"],"award-info":[{"award-number":["RG91\/22"]}]},{"name":"Singapore Ministry of Education Academic Fund","award":["62472460"],"award-info":[{"award-number":["62472460"]}]},{"name":"Singapore Ministry of Education Academic Fund","award":["62102460"],"award-info":[{"award-number":["62102460"]}]},{"name":"Singapore Ministry of Education Academic Fund","award":["2024A1515010161"],"award-info":[{"award-number":["2024A1515010161"]}]},{"name":"Singapore Ministry of Education Academic Fund","award":["2023A1515012982"],"award-info":[{"award-number":["2023A1515012982"]}]},{"name":"Singapore Ministry of Education Academic Fund","award":["RG91\/22"],"award-info":[{"award-number":["RG91\/22"]}]},{"name":"NTU startup","award":["62472460"],"award-info":[{"award-number":["62472460"]}]},{"name":"NTU startup","award":["62102460"],"award-info":[{"award-number":["62102460"]}]},{"name":"NTU startup","award":["2024A1515010161"],"award-info":[{"award-number":["2024A1515010161"]}]},{"name":"NTU startup","award":["2023A1515012982"],"award-info":[{"award-number":["2023A1515012982"]}]},{"name":"NTU startup","award":["RG91\/22"],"award-info":[{"award-number":["RG91\/22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Vertical Federated Learning (VFL) is a promising category of Federated Learning that enables collaborative model training among distributed parties with data privacy protection. Due to its unique training architecture, a key challenge of VFL is high communication cost due to transmitting intermediate results between the Active Party and Passive Parties. Current communication-efficient VFL methods rely on using stale results without meticulous selection, which can impair model accuracy, particularly in noisy data environments. To address these limitations, this work proposes VFL-Cafe, a new VFL training method that leverages dynamic caching and feature selection to boost communication efficiency and model accuracy. In each communication round, the employed caching scheme allows multiple batches of intermediate results to be cached and strategically reused by different parties, reducing the communication overhead while maintaining model accuracy. Additionally, to eliminate the negative impact of noisy features that may undermine the effectiveness of using stale results to reduce communication rounds and incur significant model degradation, a feature selection strategy is integrated into each round of local updates. Theoretical analysis is then conducted to provide guidance on cache configuration, optimizing performance. Finally, extensive experimental results validate VFL-Cafe\u2019s efficacy, demonstrating remarkable improvements in communication efficiency and model accuracy.<\/jats:p>","DOI":"10.3390\/e27010066","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T03:29:43Z","timestamp":1736825383000},"page":"66","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["VFL-Cafe: Communication-Efficient Vertical Federated Learning via Dynamic Caching and Feature Selection"],"prefix":"10.3390","volume":"27","author":[{"given":"Jiahui","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer and Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer and Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer and Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University in Singapore, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6624-9752","authenticated-orcid":false,"given":"Chee Wei","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University in Singapore, Singapore 639798, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1109\/TKDE.2024.3352628","article-title":"Vertical federated learning: Concepts, advances, and challenges","volume":"36","author":"Liu","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_2","unstructured":"Yang, L., Chai, D., Zhang, J., Jin, Y., Wang, L., Liu, H., Tian, H., Xu, Q., and Chen, K. (2023). A survey on vertical federated learning: From a layered perspective. arXiv."},{"key":"ref_3","unstructured":"Wei, K., Li, J., Ma, C., Ding, M., Wei, S., Wu, F., Chen, G., and Ranbaduge, T. (2022). Vertical federated learning: Challenges, methodologies and experiments. arXiv."},{"key":"ref_4","unstructured":"Khan, A., Thij, M.T., and Wilbik, A. (2022). Vertical federated learning: A structured literature review. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.knosys.2021.106775","article-title":"A survey on federated learning","volume":"216","author":"Zhang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s13042-022-01647-y","article-title":"A survey on federated learning: Challenges and applications","volume":"14","author":"Wen","year":"2023","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_7","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A. (2017, January 20\u201322). Communication-efficient learning of deep networks from decentralized data. Proceedings of the Artificial Intelligence and Statistics, PMLR, Ft. Lauderdale, FL, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhao, D., Tao, L., Wang, K., Qiao, S., and Zeng, X. (2024). A credible and fair federated learning framework based on blockchain. IEEE Trans. Artif. Intell., 1\u201315.","DOI":"10.1109\/TAI.2024.3355362"},{"key":"ref_9","first-page":"29995","article-title":"Delayed gradient averaging: Tolerate the communication latency for federated learning","volume":"34","author":"Zhu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fu, F., Miao, X., Jiang, J., Xue, H., and Cui, B. (2022). Towards communication-efficient vertical federated learning training via cache-enabled local updates. arXiv.","DOI":"10.14778\/3547305.3547316"},{"key":"ref_11","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Ramage, D., and Richt\u00e1rik, P. (2016). Federated optimization: Distributed machine learning for on-device intelligence. arXiv."},{"key":"ref_12","unstructured":"McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., and Bacon, D. (2016, January 5\u201310). Federated learning: Strategies for improving communication efficiency. Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain."},{"key":"ref_13","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4277","DOI":"10.1109\/TSP.2022.3198176","article-title":"Fedbcd: A communication-efficient collaborative learning framework for distributed features","volume":"70","author":"Liu","year":"2022","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"17878","DOI":"10.1109\/TNNLS.2023.3309701","article-title":"Flexible vertical federated learning with heterogeneous parties","volume":"35","author":"Castiglia","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1109\/TPDS.2022.3178443","article-title":"Adaptive vertical federated learning on unbalanced features","volume":"33","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10095","DOI":"10.1109\/JIOT.2023.3237032","article-title":"Federated feature selection for horizontal federated learning in iot networks","volume":"10","author":"Zhang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Shi, Y., Luo, J., Wang, F., Peng, C., Fan, P., and Letaief, K.B. (June, January 28). Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning. Proceedings of the ICC 2023-IEEE International Conference on Communications, Rome, Italy.","DOI":"10.1109\/ICC45041.2023.10278563"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TPDS.2020.3040887","article-title":"An efficiency-boosting client selection scheme for federated learning with fairness guarantee","volume":"32","author":"Huang","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6551","DOI":"10.1109\/TAI.2024.3436664","article-title":"Cost-Efficient Feature Selection for Horizontal Federated Learning","volume":"5","author":"Banerjee","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116109","DOI":"10.1016\/j.eswa.2021.116109","article-title":"Definition of a novel federated learning approach to reduce communication costs","volume":"189","author":"Paragliola","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1109\/TBDATA.2022.3192898","article-title":"Accelerating Vertical Federated Learning","volume":"10","author":"Cai","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Khan, A., ten Thij, M., and Wilbik, A. (2022). Communication-efficient vertical federated learning. Algorithms, 15.","DOI":"10.3390\/a15080273"},{"key":"ref_24","unstructured":"Chen, W., Ma, G., Fan, T., Kang, Y., Xu, Q., and Yang, Q. (2021). Secureboost+: A high performance gradient boosting tree framework for large scale vertical federated learning. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, Y., Wang, Y., and Pan, Y. (2020, January 13\u201315). Efficient asynchronous vertical federated learning via gradient prediction and double-end sparse compression. Proceedings of the 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV), Shenzhen, China.","DOI":"10.1109\/ICARCV50220.2020.9305383"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhu, G., and Cui, S. (2022, January 4\u20138). Low-latency cooperative spectrum sensing via truncated vertical federated learning. Proceedings of the 2022 IEEE Globecom Workshops (GC Wkshps), Rio de Janeiro, Brazil.","DOI":"10.1109\/GCWkshps56602.2022.10008685"},{"key":"ref_27","first-page":"2088","article-title":"Vf-ps: How to select important participants in vertical federated learning, efficiently and securely?","volume":"35","author":"Jiang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, K., Song, Z., Zhang, Y., Zhou, Y., Sun, X., and Wang, J. (2021, January 22\u201328). Model optimization method based on vertical federated learning. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Republic of Korea.","DOI":"10.1109\/ISCAS51556.2021.9401521"},{"key":"ref_29","unstructured":"Liu, Y., Kang, Y., Zhang, X., Li, L., Cheng, Y., Chen, T., Hong, M., and Yang, Q. (2019). A communication efficient collaborative learning framework for distributed features. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xie, C., Chen, P.Y., Li, Q., Nourian, A., Zhang, C., and Li, B. (2024, January 9\u201311). Improving privacy-preserving vertical federated learning by efficient communication with admm. Proceedings of the 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Toronto, ON, Canada.","DOI":"10.1109\/SaTML59370.2024.00029"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, A., Peng, H., Zhang, L., Huang, J., Guo, Q., Yu, H., and Liu, Y. (2023, January 17\u201320). FedSDG-FS: Efficient and secure feature selection for vertical federated learning. Proceedings of the IEEE INFOCOM 2023-IEEE Conference on Computer Communications, New York City, NY, USA.","DOI":"10.1109\/INFOCOM53939.2023.10228895"},{"key":"ref_32","unstructured":"Castiglia, T., Zhou, Y., Wang, S., Kadhe, S., Baracaldo, N., and Patterson, S. (2023, January 23\u201329). LESS-VFL: Communication-efficient feature selection for vertical federated learning. Proceedings of the International Conference on Machine Learning, PMLR, Honolulu, HI, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"118097","DOI":"10.1016\/j.eswa.2022.118097","article-title":"Vertical federated learning-based feature selection with non-overlapping sample utilization","volume":"208","author":"Feng","year":"2022","journal-title":"Expert Syst. 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