{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T18:23:24Z","timestamp":1783103004598,"version":"3.54.6"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402350"],"award-info":[{"award-number":["62402350"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2024AFB159"],"award-info":[{"award-number":["2024AFB159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023AFB795"],"award-info":[{"award-number":["2023AFB795"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Standards & Interfaces"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.csi.2026.104168","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T07:19:50Z","timestamp":1778570390000},"page":"104168","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Dynamically masked federated learning for privacy-preserving modeling on non-IID medical data"],"prefix":"10.1016","volume":"98","author":[{"given":"Kai","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8211-1359","authenticated-orcid":false,"given":"Zhiyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxiao","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yepeng","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alsharif","family":"Abuadbba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.csi.2026.104168_b1","doi-asserted-by":"crossref","first-page":"5335","DOI":"10.1109\/TNSM.2024.3422376","article-title":"Reliable federated learning with GAN model for robust and resilient future healthcare system","volume":"21","author":"Murmu","year":"2024","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"6","key":"10.1016\/j.csi.2026.104168_b2","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1109\/TMI.2024.3453419","article-title":"Knowledge-aware multisite adaptive graph transformer for brain disorder diagnosis","volume":"44","author":"Song","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.csi.2026.104168_b3","unstructured":"B. McMahan, E. Moore, D. Ramage, S. Hampson, B.A. y Arcas, Communication-efficient learning of deep networks from decentralized data, in: Proc. Artif. Intell. Stat., 2017, pp. 1273\u20131282."},{"key":"10.1016\/j.csi.2026.104168_b4","article-title":"Privet: A privacy-preserving vertical federated learning service for gradient boosted decision tables","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"6","key":"10.1016\/j.csi.2026.104168_b5","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1109\/TDSC.2020.3035591","article-title":"Toward verifiable and privacy-preserving machine learning prediction","volume":"19","author":"Niu","year":"2022","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"10.1016\/j.csi.2026.104168_b6","doi-asserted-by":"crossref","unstructured":"L. Melis, C. Song, E. De Cristofaro, V. Shmatikov, Exploiting unintended feature leakage in collaborative learning, in: Proc. IEEE Symp. Security and Privacy, SP, 2019, pp. 691\u2013706.","DOI":"10.1109\/SP.2019.00029"},{"key":"10.1016\/j.csi.2026.104168_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2025.111346","article-title":"DP-SAFL: Semi-asynchronous federated learning with differential privacy in heterogeneous edge computing","author":"He","year":"2025","journal-title":"Comput. Netw."},{"key":"10.1016\/j.csi.2026.104168_b8","doi-asserted-by":"crossref","unstructured":"Y. Shi, Y. Liu, K. Wei, L. Shen, X. Wang, D. Tao, Make Landscape Flatter in Differentially Private Federated Learning, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2023, pp. 24552\u201324562.","DOI":"10.1109\/CVPR52729.2023.02352"},{"issue":"10","key":"10.1016\/j.csi.2026.104168_b9","doi-asserted-by":"crossref","first-page":"9368","DOI":"10.1109\/TMC.2024.3361876","article-title":"FedCache: A knowledge cache-driven federated learning architecture for personalized edge intelligence","volume":"23","author":"Wu","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.csi.2026.104168_b10","unstructured":"Zhao Rui, Duan Xiaowen, Liu Xin, Zhou Rui, Zhou Qingguo, Hierarchical Sinkhorn Distance Clustering Federated Learning for Non-IID Data, J. Comput. Res. Dev. http:\/\/dx.doi.org\/10.7544\/issn1000-1239.202440969."},{"issue":"2","key":"10.1016\/j.csi.2026.104168_b11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MIS.2021.3114610","article-title":"Dynamic sampling and selective masking for communication-efficient federated learning","volume":"37","author":"Ji","year":"2022","journal-title":"IEEE Intell. Syst."},{"key":"10.1016\/j.csi.2026.104168_b12","doi-asserted-by":"crossref","unstructured":"Z. Guan, Y. Zhao, Z. Wan, et al., OPSA: Efficient and verifiable one-pass secure aggregation with TEE for federated learning, IEEE Trans. Dependable Secure Comput. http:\/\/dx.doi.org\/10.1109\/TDSC.2025.3565640.","DOI":"10.1109\/TDSC.2025.3565640"},{"issue":"3","key":"10.1016\/j.csi.2026.104168_b13","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1109\/TSC.2025.3562359","article-title":"Robust federated learning for privacy preservation and efficiency in edge computing","volume":"18","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"2","key":"10.1016\/j.csi.2026.104168_b14","first-page":"1364","article-title":"Privacy-preserving federated deep learning with irregular users","volume":"19","author":"Xu","year":"2022","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"issue":"8","key":"10.1016\/j.csi.2026.104168_b15","doi-asserted-by":"crossref","first-page":"13180","DOI":"10.1109\/JIOT.2023.3347552","article-title":"Privacy-preserving federated learning with resource adaptive compression for edge devices","volume":"11","author":"Hidayat","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"24","key":"10.1016\/j.csi.2026.104168_b16","doi-asserted-by":"crossref","first-page":"22051","DOI":"10.1109\/JIOT.2023.3296595","article-title":"Efficient privacy preserving in IoMT with blockchain and lightweight secret sharing","volume":"10","author":"Li","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.csi.2026.104168_b17","doi-asserted-by":"crossref","unstructured":"H. Hayati, C. Murguia, N. van de Wouw, Privacy-preserving federated learning via system immersion and random matrix encryption, in: Proc. IEEE Conf. Decision and Control, CDC, 2022, pp. 2998\u20133003.","DOI":"10.1109\/CDC51059.2022.9993113"},{"key":"10.1016\/j.csi.2026.104168_b18","doi-asserted-by":"crossref","unstructured":"K. Bonawitz, et al., Practical secure aggregation for privacy-preserving machine learning, in: Proc. ACM Conf. Computer and Communications Security, CCS, 2017, pp. 1175\u20131191.","DOI":"10.1145\/3133956.3133982"},{"key":"10.1016\/j.csi.2026.104168_b19","unstructured":"J.A. Primault, et al., Secure summation in the presence of malicious parties, in: Proc. 13th Int. Conf. Trust, Security and Privacy in Computing and Communications (TrustCom), 2014, pp. 283\u2013290."},{"issue":"23","key":"10.1016\/j.csi.2026.104168_b20","first-page":"23402","article-title":"S-FL: A secure and scalable federated learning framework for industrial IoT","volume":"9","author":"Guo","year":"2022","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"10.1016\/j.csi.2026.104168_b21","first-page":"200","article-title":"Standing on the shoulders of giants: Trusted execution and key management in federated learning","volume":"74","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Comput."},{"key":"10.1016\/j.csi.2026.104168_b22","series-title":"Advances in Neural Information Processing Systems (NeurIPS)","article-title":"On the interplay between privacy and accuracy in federated learning","author":"Jiang","year":"2022"},{"issue":"9","key":"10.1016\/j.csi.2026.104168_b23","doi-asserted-by":"crossref","first-page":"3388","DOI":"10.1109\/TMC.2021.3056991","article-title":"User-level privacy-preserving federated learning: Analysis and performance optimization","volume":"21","author":"Wei","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"4","key":"10.1016\/j.csi.2026.104168_b24","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/TCSS.2022.3216802","article-title":"Blockchain-based two-stage federated learning for smart city applications","volume":"10","author":"Lian","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"10.1016\/j.csi.2026.104168_b25","article-title":"A secure healthcare 5.0 system based on blockchain technology entangled with federated learning","volume":"100","author":"Rehman","year":"2022","journal-title":"Comput. Electr. Eng."},{"issue":"4","key":"10.1016\/j.csi.2026.104168_b26","first-page":"73","article-title":"Blockchain-based personalized federated learning for internet of vehicles","volume":"40","author":"Usama","year":"2023","journal-title":"IEEE Softw."},{"issue":"9","key":"10.1016\/j.csi.2026.104168_b27","doi-asserted-by":"crossref","first-page":"8886","DOI":"10.1109\/TMC.2024.3357499","article-title":"Coraldb: A collaborative database for data sharing based on permissioned blockchain","volume":"23","author":"Li","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.csi.2026.104168_b28","doi-asserted-by":"crossref","unstructured":"A. Li, et al., FedMask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking, in: Proc. 19th ACM Conf. Embedded Networked Sensor Syst. (SenSys), 2021, pp. 509\u2013523.","DOI":"10.1145\/3485730.3485929"},{"key":"10.1016\/j.csi.2026.104168_b29","first-page":"473","article-title":"End-to-end privacy preserving deep learning on multi-institutional medical imaging","volume":"3","author":"Kaissis","year":"2021"},{"key":"10.1016\/j.csi.2026.104168_b30","series-title":"Proc. Int. Conf. Artificial Intelligence and Statistics","article-title":"Federated learning with buffered asynchronous aggregation","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.csi.2026.104168_b31","series-title":"Asynchronous federated optimization","author":"Xie","year":"2019"},{"key":"10.1016\/j.csi.2026.104168_b32","series-title":"Proc. Conf. on Machine Learning and Systems (MLSys)","article-title":"Federated optimization in heterogeneous networks","author":"Li","year":"2020"},{"key":"10.1016\/j.csi.2026.104168_b33","series-title":"Proc. IEEE\/CVF Conf. Computer Vision and Pattern Recognition","article-title":"Model-contrastive federated learning","author":"Li","year":"2021"}],"container-title":["Computer Standards & Interfaces"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0920548926000425?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0920548926000425?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:57:32Z","timestamp":1783101452000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0920548926000425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":33,"alternative-id":["S0920548926000425"],"URL":"https:\/\/doi.org\/10.1016\/j.csi.2026.104168","relation":{},"ISSN":["0920-5489"],"issn-type":[{"value":"0920-5489","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dynamically masked federated learning for privacy-preserving modeling on non-IID medical data","name":"articletitle","label":"Article Title"},{"value":"Computer Standards & Interfaces","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.csi.2026.104168","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104168"}}