Fair Detection of Poisoning Attacks in Federated Learning
作者： Ashneet Khandpur Singh , Alberto Blanco-Justicia , Josep Domingo-Ferrer , David Sanchez , David Rebollo-Monedero
DOI: 10.1109/ICTAI50040.2020.00044
关键词:
摘要: … between anti-poisoning and diversity accommodation. By including diverse clients, we aim at making it possible to learn less discriminatory machine learning models. We present two …
sci-hub.st 参考文章(12)
David Arthur, Sergei Vassilvitskii, k-means++: the advantages of careful seeding symposium on discrete algorithms. pp. 1027- 1035 ,(2007) , 10.5555/1283383.1283494
Jin Huang, C.X. Ling, Using AUC and accuracy in evaluating learning algorithms IEEE Transactions on Knowledge and Data Engineering. ,vol. 17, pp. 299- 310 ,(2005) , 10.1109/TKDE.2005.50
Semhar Michael, Volodymyr Melnykov, An effective strategy for initializing the EM algorithm in finite mixture models Advanced Data Analysis and Classification. ,vol. 10, pp. 563- 583 ,(2016) , 10.1007/S11634-016-0264-8
Jakub Konečný, Ananda Theertha Suresh, Dave Bacon, Felix X. Yu, Peter Richtarik, H. Brendan McMahan, Federated Learning: Strategies for Improving Communication Efficiency arXiv: Learning. ,(2016)
Blaise Aguera y Arcas, Daniel Ramage, H. Brendan McMahan, Seth Hampson, Eider Moore, Communication-Efficient Learning of Deep Networks from Decentralized Data international conference on artificial intelligence and statistics. pp. 1273- 1282 ,(2017)
Yudong Chen, Lili Su, Jiaming Xu, Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent measurement and modeling of computer systems. ,vol. 1, pp. 96- 96 ,(2018) , 10.1145/3219617.3219655
Rachid Guerraoui, Julien Stainer, Peva Blanchard, El Mahdi El Mhamdi, Machine learning with adversaries: byzantine tolerant gradient descent neural information processing systems. ,vol. 30, pp. 118- 128 ,(2017)
Deborah Estrin, Eugene Bagdasaryan, Yiqing Hua, Vitaly Shmatikov, Andreas Veit, How To Backdoor Federated Learning arXiv: Cryptography and Security. ,(2018)
Daniel Gildea, Iftekhar Naim, Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients arXiv: Learning. ,(2012)
Prateek Mittal, Seraphin B. Calo, Supriyo Chakraborty, Arjun Nitin Bhagoji, Analyzing Federated Learning through an Adversarial Lens. international conference on machine learning. pp. 634- 643 ,(2019)