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[1] William J. Dally,et al. Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training , 2017, ICLR.
[2] Prateek Saxena,et al. Auror: defending against poisoning attacks in collaborative deep learning systems , 2016, ACSAC.
[3] Vitaly Shmatikov,et al. Privacy-preserving deep learning , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[4] Shiho Moriai,et al. Privacy-Preserving Deep Learning: Revisited and Enhanced , 2017, ATIS.
[5] Yoshua Bengio,et al. Challenges in representation learning: A report on three machine learning contests , 2013, Neural Networks.
[6] Reza Shokri,et al. Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks , 2018, ArXiv.
[7] Giovanni Felici,et al. Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers , 2013, Int. J. Secur. Networks.
[8] Kaiming He,et al. Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[9] Vitaly Shmatikov,et al. Exploiting Unintended Feature Leakage in Collaborative Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[10] Li Zhang,et al. Learning Differentially Private Language Models Without Losing Accuracy , 2017, ArXiv.
[11] Cispa Helmholtz. Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning , 2019 .
[12] Giuseppe Ateniese,et al. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning , 2017, CCS.
[13] Ahmad-Reza Sadeghi,et al. DIoT: A Self-learning System for Detecting Compromised IoT Devices , 2018 .
[14] Davide Anguita,et al. A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.
[15] H. Brendan McMahan,et al. Learning Differentially Private Recurrent Language Models , 2017, ICLR.
[16] Tassilo Klein,et al. Differentially Private Federated Learning: A Client Level Perspective , 2017, ArXiv.
[17] Moran Baruch,et al. A Little Is Enough: Circumventing Defenses For Distributed Learning , 2019, NeurIPS.
[18] Samuel Marchal,et al. DÏoT: A Federated Self-learning Anomaly Detection System for IoT , 2018, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).
[19] Emiliano De Cristofaro,et al. Knock Knock, Who's There? Membership Inference on Aggregate Location Data , 2017, NDSS.
[20] Nikolaos G. Bourbakis,et al. A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[21] Yaoliang Yu,et al. Petuum: A New Platform for Distributed Machine Learning on Big Data , 2015, IEEE Trans. Big Data.
[22] Walid Saad,et al. Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications , 2018, IEEE Transactions on Communications.
[23] Amir Houmansadr,et al. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[24] Vitaly Shmatikov,et al. How To Backdoor Federated Learning , 2018, AISTATS.
[25] Prateek Mittal,et al. Analyzing Federated Learning through an Adversarial Lens , 2018, ICML.
[26] Payman Mohassel,et al. SecureML: A System for Scalable Privacy-Preserving Machine Learning , 2017, 2017 IEEE Symposium on Security and Privacy (SP).
[27] Trishul M. Chilimbi,et al. Project Adam: Building an Efficient and Scalable Deep Learning Training System , 2014, OSDI.
[28] Harald Kittler,et al. Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .
[29] Hao Deng,et al. LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data , 2018, ArXiv.
[30] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[31] Marc'Aurelio Ranzato,et al. Large Scale Distributed Deep Networks , 2012, NIPS.
[32] Li Huang,et al. LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data , 2018, PloS one.
[33] Michael I. Jordan,et al. SparkNet: Training Deep Networks in Spark , 2015, ICLR.
[34] Yang Song,et al. Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning , 2018, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[35] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[36] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Sarvar Patel,et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..
[38] Walid Saad,et al. Federated Learning for Ultra-Reliable Low-Latency V2V Communications , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).
[39] Rachid Guerraoui,et al. Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent , 2017, NIPS.
[40] Hubert Eichner,et al. Federated Learning for Mobile Keyboard Prediction , 2018, ArXiv.
[41] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[42] Ivan Beschastnikh,et al. Mitigating Sybils in Federated Learning Poisoning , 2018, ArXiv.
[43] Emiliano De Cristofaro,et al. LOGAN: Membership Inference Attacks Against Generative Models , 2017, Proc. Priv. Enhancing Technol..
[44] Saeed Mahloujifar,et al. Multi-party Poisoning through Generalized p-Tampering , 2018, IACR Cryptol. ePrint Arch..
[45] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[46] Wenqi Wei,et al. Demystifying Membership Inference Attacks in Machine Learning as a Service , 2019, IEEE Transactions on Services Computing.
[47] Gang Wang,et al. LEMNA: Explaining Deep Learning based Security Applications , 2018, CCS.
[48] Alexander J. Smola,et al. Parallelized Stochastic Gradient Descent , 2010, NIPS.
[49] Mikhail Belkin,et al. Learning privately from multiparty data , 2016, ICML.
[50] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[51] Nikita Borisov,et al. Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations , 2018, CCS.
[52] Swaroop Ramaswamy,et al. Federated Learning for Emoji Prediction in a Mobile Keyboard , 2019, ArXiv.