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Dou, \u201cFedBN: Federated learning on non-IID features via local batch normalization,\u201d 2021. doi: 10.48550\/arXiv.2102.07623."},{"key":"ref25","series-title":"2022 IEEE 42nd Int. Conf. Distrib. Comput. Syst. Workshops (ICDCSW)","first-page":"193","article-title":"Privacy-preserving blockchain-based global data sharing for federated learning with non-IID data","author":"Lian","year":"2022"},{"key":"ref26","first-page":"5572","article-title":"CBFL: A communication efficient federated learning framework from data redundancy perspective","volume":"19","author":"Li","year":"2021","journal-title":"IEEE Syst. J."},{"key":"ref27","doi-asserted-by":"crossref","first-page":"17","DOI":"10.29012\/jpc.v7i3.405","article-title":"Calibrating noise to sensitivity in private data analysis","volume":"7","author":"Dwork","year":"2006","journal-title":"J. Priv. 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De Cristofaro, \u201cLocal and central differential privacy for robustness and privacy in federated learning,\u201d 2020. doi: 10.48550\/arXiv.2009.035."},{"key":"ref32","first-page":"1","article-title":"Augmented multi-party computation against gradient leakage in federated learning","volume":"99","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1613\/jair.1.14649","article-title":"How to DP-fy ML: A practical guide to machine learning with differential privacy","volume":"77","author":"Ponomareva","year":"2023","journal-title":"J. Artifi. Intell. Res."},{"key":"ref34","first-page":"61","article-title":"LDP-Fed: Federated learning with local differential privacy","volume":"6","author":"Truex","year":"2020","journal-title":"Proc. Third ACM Int. Workshop Edge Syst. Anal. Network."},{"key":"ref35","series-title":"31st USENIX Secur. Symp. (USENIX Security 22)","first-page":"1397","article-title":"Label inference attacks against vertical federated learning","author":"Fu","year":"2022"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"11633","DOI":"10.1109\/TITS.2021.3105682","article-title":"Privacy-preserving deep learning model for decentralized vanets using fully homomorphic encryption and blockchain","volume":"23","author":"Chen","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref37","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ins.2020.03.074","article-title":"Privacy-preserving distributed deep learning based on secret sharing","volume":"527","author":"Duan","year":"2020","journal-title":"Inf. Sci."},{"key":"ref38","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","article-title":"Secure, privacy-preserving and federated machine learning in medical imaging","volume":"2","author":"Kaissis","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref39","series-title":"Proc. 22nd ACM SIGSAC Conf. Comput. Commun. Secur. Assoc. Comput. Mach.","first-page":"1310","article-title":"Privacy preserving deep learning","author":"Shokri","year":"2015"},{"key":"ref40","series-title":"Proc. 2016 ACM SIGSAC Conf. Comput. Commun. Secur. (CCS \u201916)","first-page":"308","article-title":"Deep learning with differential privacy","author":"Abadi","year":"2016"},{"key":"ref41","unstructured":"R. C. Geyer, T. Klein, and M. Nabi, \u201cDifferentially private federated learning: A client level perspective,\u201d 2017. doi: 10.48550\/arXiv.1712.07557."},{"key":"ref42","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"66","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref43","first-page":"1735","article-title":"Long short-term memory","volume":"8","author":"Hochreiter","year":"2010","journal-title":"Neural Comput."},{"key":"ref44","unstructured":"H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni, \u201cFederated learning with matched averaging,\u201d 2020. doi: 10.48550\/arXiv.2002.06440."},{"key":"ref45","unstructured":"Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin and V. Chandra, \u201cFederated learning with noniid data,\u201d 2018. doi:10.48550\/arXiv.1806.00582."},{"key":"ref46","unstructured":"S. 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