{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T07:15:17Z","timestamp":1783149317311,"version":"3.54.6"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100015286","name":"Hebei Provincial Key Research Projects","doi-asserted-by":"publisher","award":["22340701D"],"award-info":[{"award-number":["22340701D"]}],"id":[{"id":"10.13039\/501100015286","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L222114"],"award-info":[{"award-number":["L222114"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2023201033"],"award-info":[{"award-number":["F2023201033"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2022201005"],"award-info":[{"award-number":["F2022201005"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Future Generation Computer Systems"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.future.2026.108576","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T15:02:53Z","timestamp":1777993373000},"page":"108576","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Accurate, differentially private federated learning via Adaptive Polynomial and Dynamic Color Transformations"],"prefix":"10.1016","volume":"183","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5427-1272","authenticated-orcid":false,"given":"Yibo","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjie","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"XiaoYan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2599-1475","authenticated-orcid":false,"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.future.2026.108576_b1","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","article-title":"Algorithmic fairness in artificial intelligence for medicine and healthcare","volume":"7","author":"Chen","year":"2023","journal-title":"Nat. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.future.2026.108576_b2","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-023-3282-7","article-title":"A survey on federated learning: a perspective from multi-party computation","volume":"18","author":"Liu","year":"2024","journal-title":"Front. Comput. Sci."},{"key":"10.1016\/j.future.2026.108576_b3","series-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u1ef3","year":"2016"},{"key":"10.1016\/j.future.2026.108576_b4","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.future.2026.108576_b5","doi-asserted-by":"crossref","unstructured":"M. Badar, S. Sikdar, W. Nejdl, M. Fisichella, Fairtrade: Achieving pareto-optimal trade-offs between balanced accuracy and fairness in federated learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, 2024, pp. 10962\u201310970.","DOI":"10.1609\/aaai.v38i10.28971"},{"key":"10.1016\/j.future.2026.108576_b6","doi-asserted-by":"crossref","unstructured":"Y.H. Ezzeldin, S. Yan, C. He, E. Ferrara, A.S. Avestimehr, Fairfed: Enabling group fairness in federated learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2023, pp. 7494\u20137502.","DOI":"10.1609\/aaai.v37i6.25911"},{"key":"10.1016\/j.future.2026.108576_b7","series-title":"2019 IEEE Symposium on Security and Privacy","first-page":"739","article-title":"Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning","author":"Nasr","year":"2019"},{"key":"10.1016\/j.future.2026.108576_b8","first-page":"16937","article-title":"Inverting gradients-how easy is it to break privacy in federated learning?","volume":"33","author":"Geiping","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.future.2026.108576_b9","series-title":"International Conference on Machine Learning","first-page":"5959","article-title":"Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix","author":"Lam","year":"2021"},{"key":"10.1016\/j.future.2026.108576_b10","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"5749","article-title":"Private non-convex federated learning without a trusted server","author":"Lowy","year":"2023"},{"key":"10.1016\/j.future.2026.108576_b11","series-title":"Computer Security\u2013ESORICs 2020: 25th European Symposium on Research in Computer Security, ESORICs 2020, Guildford, UK, September 14\u201318, 2020, Proceedings, Part I 25","first-page":"480","article-title":"Data poisoning attacks against federated learning systems","author":"Tolpegin","year":"2020"},{"key":"10.1016\/j.future.2026.108576_b12","series-title":"Advances in Cryptology-EUROCRYPT 2006: 24th Annual International Conference on the Theory and Applications of Cryptographic Techniques, St. Petersburg, Russia, May 28-June 1, 2006. Proceedings 25","first-page":"486","article-title":"Our data, ourselves: Privacy via distributed noise generation","author":"Dwork","year":"2006"},{"key":"10.1016\/j.future.2026.108576_b13","unstructured":"T. Stevens, C. Skalka, C. Vincent, J. Ring, S. Clark, J. Near, Efficient differentially private secure aggregation for federated learning via hardness of learning with errors, in: 31st USENIX Security Symposium (USENIX Security 22), 2022, pp. 1379\u20131395."},{"key":"10.1016\/j.future.2026.108576_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.103715","article-title":"Efficient federated learning privacy preservation method with heterogeneous differential privacy","volume":"139","author":"Ling","year":"2024","journal-title":"Comput. Secur."},{"key":"10.1016\/j.future.2026.108576_b15","doi-asserted-by":"crossref","unstructured":"H. Chen, H. Vikalo, Federated learning in non-iid settings aided by differentially private synthetic data, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 5027\u20135036.","DOI":"10.1109\/CVPRW59228.2023.00531"},{"key":"10.1016\/j.future.2026.108576_b16","doi-asserted-by":"crossref","unstructured":"J. Liu, J. Lou, L. Xiong, J. Liu, X. Meng, Cross-silo federated learning with record-level personalized differential privacy, in: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024, pp. 303\u2013317.","DOI":"10.1145\/3658644.3670351"},{"key":"10.1016\/j.future.2026.108576_b17","series-title":"Utility fairness for the differentially private federated learning","author":"Alvi","year":"2021"},{"key":"10.1016\/j.future.2026.108576_b18","unstructured":"Y. Yang, B. Hui, H. Yuan, N. Gong, Y. Cao, {PrivateFL}: Accurate, differentially private federated learning via personalized data transformation, in: 32nd USENIX Security Symposium (USENIX Security 23), 2023, pp. 1595\u20131612."},{"key":"10.1016\/j.future.2026.108576_b19","doi-asserted-by":"crossref","DOI":"10.1109\/TIFS.2024.3420246","article-title":"Metricizing the Euclidean space towards desired distance relations in point clouds","author":"Rass","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.future.2026.108576_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2024.109511","article-title":"Pearson\u2013Matthews correlation coefficients for binary and multinary classification","volume":"222","author":"Stoica","year":"2024","journal-title":"Signal Process."},{"key":"10.1016\/j.future.2026.108576_b21","series-title":"International Conference on Machine Learning","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","author":"Karimireddy","year":"2020"},{"key":"10.1016\/j.future.2026.108576_b22","series-title":"Fedbn: Federated learning on non-iid features via local batch normalization","author":"Li","year":"2021"},{"key":"10.1016\/j.future.2026.108576_b23","series-title":"European Conference on Computer Vision","first-page":"179","article-title":"Addressing heterogeneity in federated learning via distributional transformation","author":"Yuan","year":"2022"},{"key":"10.1016\/j.future.2026.108576_b24","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"10110","article-title":"Differentially private federated learning on heterogeneous data","author":"Noble","year":"2022"},{"key":"10.1016\/j.future.2026.108576_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111917","article-title":"Fusion of low-rankness and smoothness under learnable nonlinear transformation for tensor completion","volume":"296","author":"Zhang","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.future.2026.108576_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2024.109400","article-title":"Tensor recovery using the tensor nuclear norm based on nonconvex and nonlinear transformations","volume":"219","author":"Tu","year":"2024","journal-title":"Signal Process."},{"key":"10.1016\/j.future.2026.108576_b27","series-title":"Cobias and debias: Minimizing language model pairwise accuracy bias via nonlinear integer programming","author":"Lin","year":"2024"},{"key":"10.1016\/j.future.2026.108576_b28","doi-asserted-by":"crossref","first-page":"154975","DOI":"10.1109\/ACCESS.2020.3018874","article-title":"On box-cox transformation for image normality and pattern classification","volume":"8","author":"Cheddad","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.future.2026.108576_b29","series-title":"International Conference on Intelligent Computing","first-page":"431","article-title":"PDT-DPFL: Using polynomial data transformations realize accurate, differentially private federated learning","author":"Liu","year":"2025"},{"key":"10.1016\/j.future.2026.108576_b30","series-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017"},{"key":"10.1016\/j.future.2026.108576_b31","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","article-title":"Federated learning with differential privacy: Algorithms and performance analysis","volume":"15","author":"Wei","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.future.2026.108576_b32","series-title":"LDP-FL: Practical private aggregation in federated learning with local differential privacy","author":"Sun","year":"2020"},{"key":"10.1016\/j.future.2026.108576_b33","doi-asserted-by":"crossref","unstructured":"M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, L. Zhang, Deep learning with differential privacy, in: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016, pp. 308\u2013318.","DOI":"10.1145\/2976749.2978318"},{"issue":"1\u20132","key":"10.1016\/j.future.2026.108576_b34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. Trends Mach. Learn."},{"key":"10.1016\/j.future.2026.108576_b35","series-title":"2019 International Conference on Advanced Computing and Applications","first-page":"97","article-title":"Differential privacy in deep learning: an overview","author":"Ha","year":"2019"},{"key":"10.1016\/j.future.2026.108576_b36","series-title":"Making the shoe fit: Architectures, initializations, and tuning for learning with privacy","author":"Papernot","year":"2020"},{"key":"10.1016\/j.future.2026.108576_b37","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1023\/A:1026543900054","article-title":"The earth mover\u2019s distance as a metric for image retrieval","volume":"40","author":"Rubner","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.future.2026.108576_b38","series-title":"FedPIA\u2013permuting and integrating adapters leveraging wasserstein barycenters for finetuning foundation models in Multi-Modal federated learning","author":"Saha","year":"2024"},{"key":"10.1016\/j.future.2026.108576_b39","series-title":"Opacus: User-friendly differential privacy library in PyTorch","author":"Yousefpour","year":"2021"}],"container-title":["Future Generation Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X26002104?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X26002104?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T06:17:18Z","timestamp":1783145838000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167739X26002104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":39,"alternative-id":["S0167739X26002104"],"URL":"https:\/\/doi.org\/10.1016\/j.future.2026.108576","relation":{},"ISSN":["0167-739X"],"issn-type":[{"value":"0167-739X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Accurate, differentially private federated learning via Adaptive Polynomial and Dynamic Color Transformations","name":"articletitle","label":"Article Title"},{"value":"Future Generation Computer Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.future.2026.108576","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":"108576"}}