{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T16:09:42Z","timestamp":1768406982382,"version":"3.49.0"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Dependable and Secure Comput."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1109\/tdsc.2024.3419211","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T17:54:04Z","timestamp":1719856444000},"page":"939-949","source":"Crossref","is-referenced-by-count":4,"title":["FedPA: Generator-Based Heterogeneous Federated Prototype Adversarial Learning"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4649-1162","authenticated-orcid":false,"given":"Lei","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9793-8203","authenticated-orcid":false,"given":"Xiaoding","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2735-2359","authenticated-orcid":false,"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Minjiang University, Fuzhou, China"}]},{"given":"Jiwu","family":"Shu","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Minjiang University, Fuzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1716-1399","authenticated-orcid":false,"given":"Hui","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7351-5724","authenticated-orcid":false,"given":"Xun","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University, Melbourne, VIC, Australia"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018"},{"key":"ref3","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref5","first-page":"4519","article-title":"Tighter theory for local SGD on identical and heterogeneous data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Khaled"},{"key":"ref6","article-title":"FedGAN: Federated generative adversarial networks for distributed data","author":"Rasouli","year":"2020"},{"key":"ref7","article-title":"FedDTG: Federated data-free knowledge distillation via three-player generative adversarial networks","author":"Zhang","year":"2022"},{"key":"ref8","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref9","article-title":"Think locally, act globally: Federated learning with local and global representations","author":"Liang","year":"2020"},{"key":"ref10","article-title":"Federated learning with personalization layers","author":"Arivazhagan","year":"2019"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00980"},{"key":"ref12","article-title":"Distilling the knowledge in a neural network","volume-title":"Proc. NIPS Deep Learn. Representation Learn. Workshop","author":"Hinton"},{"key":"ref13","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref15","article-title":"The best of both worlds: Accurate global and personalized models through federated learning with data-free hyper-knowledge distillation","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref16","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref17","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref18","article-title":"Fed-ensemble: Improving generalization through model ensembling in federated learning","author":"Shi","year":"2021"},{"key":"ref19","first-page":"38461","article-title":"Preservation of the global knowledge by not-true distillation in federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lee"},{"key":"ref20","first-page":"12967","article-title":"Class-wise adaptive self distillation for heterogeneous federated learning","volume-title":"Proc. 36th AAAI Conf. Artif. Intell.","volume":"22","author":"He"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25891"},{"key":"ref24","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref26","article-title":"FedMix: Approximation of mixup under mean augmented federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yoon"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00152"},{"key":"ref29","article-title":"Federated adversarial domain adaptation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Peng"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"}],"container-title":["IEEE Transactions on Dependable and Secure Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8858\/10925471\/10579863.pdf?arnumber=10579863","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T21:21:09Z","timestamp":1742246469000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10579863\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":31,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tdsc.2024.3419211","relation":{},"ISSN":["1545-5971","1941-0018","2160-9209"],"issn-type":[{"value":"1545-5971","type":"print"},{"value":"1941-0018","type":"electronic"},{"value":"2160-9209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3]]}}}