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Communications","first-page":"63","article-title":"When edge meets learning: adaptive control for resource-constrained distributed machine learning","author":"Wang"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MC.2023.3324975","article-title":"Exploring threats, defenses, and privacy-preserving techniques in federated learning: a survey","volume":"57","author":"Huang","year":"2024 Apr","journal-title":"Computer"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: a comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun Surv Tutorials"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"100685","DOI":"10.1016\/j.cosrev.2024.100685","article-title":"Unleashing the prospective of blockchain-federated learning fusion for IoT security: a comprehensive review","volume":"54","author":"Gupta","year":"2024","journal-title":"Comput Sci Rev"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"Dwork","year":"2014","journal-title":"Foundat Trends\u24c7 Theoret Comput Sci"},{"key":"ref8","series-title":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","first-page":"308","article-title":"Deep learning with differential privacy","author":"Abadi","year":"2016"},{"key":"ref9","series-title":"International Conference on Machine Learning, ICML 2022","article-title":"Understanding clipping for federated learning: convergence and client-level differential privacy","author":"Zhang"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.1109\/TPDS.2023.3273490","article-title":"Securing distributed SGD against gradient leakage threats","volume":"34","author":"Wei","year":"2023 Jul","journal-title":"IEEE Trans Parall Distrib Syst"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/TIFS.2023.3318944","article-title":"Differentially private federated learning with an adaptive noise mechanism","volume":"19","author":"Xue","year":"2024","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"ref12","series-title":"2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","first-page":"656","article-title":"Adap DP-FL: differentially private federated learning with adaptive noise","author":"Fu","year":"2022 Dec"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"110822","DOI":"10.1016\/j.comnet.2024.110822","article-title":"Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism","volume":"254","author":"Wang","year":"2024","journal-title":"Comput Netw"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"26770","DOI":"10.1038\/s41598-024-77428-0","article-title":"Federated learning with differential privacy via fast fourier transform for tighter-efficient combining","volume":"14","author":"Guo","year":"2024","journal-title":"Sci Rep"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"103715","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":"ref16","doi-asserted-by":"crossref","first-page":"4567","DOI":"10.1109\/TKDE.2024.3379001","article-title":"Differentially private federated learning on non-iid data: convergence analysis and adaptive optimization","volume":"36","author":"Chen","year":"2024 Sep","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref17","series-title":"Proceedings of Machine Learning Research","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2016"},{"key":"ref18","series-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security","article-title":"Practical secure aggregation for privacy-preserving machine learning","author":"Bonawitz","year":"2017"},{"key":"ref19","series-title":"2023 IEEE Symposium on Security and Privacy (SP)","first-page":"477","article-title":"Flamingo: multi-round single-server secure aggregation with applications to private federated learning","author":"Ma","year":"2023 May"},{"key":"ref20","first-page":"100","author":"Phong","journal-title":"Applications and techniques in information security"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Hu R, Gong Y, Guo Y. Federated learning with sparsification-amplified privacy and adaptive optimization. arXiv preprint arXiv:2008.01558. 2020.","DOI":"10.24963\/ijcai.2021\/202"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"103067","DOI":"10.1016\/j.sysarc.2024.103067","article-title":"CLFLDP: communication-efficient layer clipping federated learning with local differential privacy","volume":"148","author":"Chen","year":"2024","journal-title":"J Syst Archit"},{"key":"ref23","first-page":"1679","article-title":"A differential privacy federated learning scheme based on adaptive gaussian noise","volume":"138","author":"Jiao","year":"2023","journal-title":"Comput Model Eng Sci"},{"key":"ref24","series-title":"International Conference on Theory and Applications of Models of Computation","first-page":"1","article-title":"Differential privacy: a survey of results","author":"Dwork","year":"2008"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1109\/TIFS.2022.3169911","article-title":"A differentially private framework for deep learning with convexified loss functions","volume":"17","author":"Lu","year":"2022","journal-title":"IEEE Trans Inf Forens Secur"},{"key":"ref26","series-title":"The 28th International Joint Conference on Artificial Intelligence (IJCAI-19);","article-title":"Heterogeneous gaussian mechanism: preserving differential privacy in deep learning with provable robustness","author":"Phan","year":"2019"},{"key":"ref27","series-title":"International Conference on Learning Representations","article-title":"Do not let privacy overbill utility: gradient embedding perturbation for private learning","author":"Yu","year":"2021"},{"key":"ref28","series-title":"2017 IEEE International Conference on Data Mining (ICDM)","first-page":"385","article-title":"Adaptive laplace mechanism: differential privacy preservation in deep learning","author":"Phan","year":"2017 Nov"},{"key":"ref29","series-title":"2024 American Control Conference (ACC)","first-page":"1807","article-title":"Trade-off between privacy and accuracy in resilient vector consensus","author":"Liu","year":"2024"},{"key":"ref30","unstructured":"McMahan HB, Ramage D, Talwar K, Zhang L. Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963. 2017."},{"key":"ref31","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":"ref32","article-title":"PPeFL: privacy-preserving edge federated learning with local differential privacy","author":"Wang","year":"2023","journal-title":"IEEE Internet Things J"},{"key":"ref33","article-title":"Privacy-preserving federated learning based on differential privacy and momentum gradient descent","author":"Weng","year":"2023","journal-title":"2022 International Joint Conference on Neural Networks (IJCNN)"},{"key":"ref34","doi-asserted-by":"crossref","DOI":"10.3390\/e25030485","article-title":"PLDP-FL: federated learning with personalized local differential privacy","volume":"25","author":"Shen","year":"2023","journal-title":"Entropy"},{"key":"ref35","first-page":"34461","author":"Malekmohammadi","year":"2024","journal-title":"Noise-aware algorithm for heterogeneous differentially private federated learning"},{"key":"ref36","unstructured":"McMahan HB, Moore E, Ramage D, y Arcas BA. Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629v1. 2016."},{"key":"ref37","unstructured":"Mironov I, Talwar K, Zhang L. R\u00e9nyi differential privacy of the sampled gaussian mechanism. arXiv preprint arXiv:1908.10530. 2019."},{"key":"ref38","series-title":"2017 IEEE 30th Computer Security Foundations Symposium (CSF)","first-page":"263","article-title":"R\u00e9nyi differential privacy","author":"Mironov","year":"2017 Aug"},{"key":"ref39","unstructured":"Zhou Y, Liu X, Fu Y, Wu D, Li C, Yu S. Optimizing the numbers of queries and replies in federated learning with differential privacy. arXiv preprint arXiv:2107.01895. 2021."},{"key":"ref40","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc IEEE"},{"key":"ref41","unstructured":"Xiao H, Rasul K, Vollgraf R. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. 2017. doi:10.48550\/arXiv.1708.07747."},{"key":"ref42","unstructured":"Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images. In: Handbook of systemic autoimmune diseases; 2009."},{"key":"ref43","unstructured":"Caldas S, Duddu SMK, Wu P, Li T, Kone\u010dn\u1ef3 J, McMahan HB, et al. LEAF: a benchmark for federated settings. arXiv preprint arXiv:1812.01097. 2018."},{"key":"ref44","series-title":"International Conference on Machine Learning","first-page":"5201","article-title":"The distributed discrete gaussian mechanism for federated learning with secure aggregation","author":"Kairouz","year":"2021"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-3\/TSP_CMC_60380\/TSP_CMC_60380.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:30:00Z","timestamp":1763101800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n3\/59922"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.060380","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}