Threats to Federated Learning

[1]  Lingjuan Lyu,et al.  Collaborative Fairness in Federated Learning , 2020, Federated Learning.

[2]  Lingjuan Lyu,et al.  How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning , 2020, IEEE Transactions on Dependable and Secure Computing.

[3]  Lingjuan Lyu,et al.  FORESEEN: Towards Differentially Private Deep Inference for Intelligent Internet of Things , 2020, IEEE Journal on Selected Areas in Communications.

[4]  Mi Zhang,et al.  Privacy Risks of General-Purpose Language Models , 2020, 2020 IEEE Symposium on Security and Privacy (SP).

[5]  Jun Zhao,et al.  Local Differential Privacy-Based Federated Learning for Internet of Things , 2020, IEEE Internet of Things Journal.

[6]  Tianjian Chen,et al.  FedVision: An Online Visual Object Detection Platform Powered by Federated Learning , 2020, AAAI.

[7]  Bo Zhao,et al.  iDLG: Improved Deep Leakage from Gradients , 2020, ArXiv.

[8]  Amir Houmansadr,et al.  Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer , 2019, ArXiv.

[9]  Yang Liu,et al.  Federated Learning , 2019, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[10]  Richard Nock,et al.  Advances and Open Problems in Federated Learning , 2019, Found. Trends Mach. Learn..

[11]  Han Yu,et al.  Privacy-preserving Heterogeneous Federated Transfer Learning , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[12]  Anit Kumar Sahu,et al.  Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.

[13]  K. S. Ng,et al.  Towards Fair and Privacy-Preserving Federated Deep Models , 2019, IEEE Transactions on Parallel and Distributed Systems.

[14]  Song Han,et al.  Deep Leakage from Gradients , 2019, NeurIPS.

[15]  Lingjuan Lyu,et al.  Fog-Embedded Deep Learning for the Internet of Things , 2019, IEEE Transactions on Industrial Informatics.

[16]  Blaine Nelson,et al.  Adversarial machine learning , 2019, AISec '11.

[17]  Qiang Yang,et al.  Federated Machine Learning , 2019, ACM Trans. Intell. Syst. Technol..

[18]  Lili Su,et al.  Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent , 2019, PERV.

[19]  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).

[20]  Gaurav Kapoor,et al.  Protection Against Reconstruction and Its Applications in Private Federated Learning , 2018, ArXiv.

[21]  Prateek Mittal,et al.  Analyzing Federated Learning through an Adversarial Lens , 2018, ICML.

[22]  Kamyar Azizzadenesheli,et al.  signSGD with Majority Vote is Communication Efficient and Fault Tolerant , 2018, ICLR.

[23]  Ivan Beschastnikh,et al.  Mitigating Sybils in Federated Learning Poisoning , 2018, ArXiv.

[24]  Vitaly Shmatikov,et al.  How To Backdoor Federated Learning , 2018, AISTATS.

[25]  Sanjiv Kumar,et al.  cpSGD: Communication-efficient and differentially-private distributed SGD , 2018, NeurIPS.

[26]  Vitaly Shmatikov,et al.  Exploiting Unintended Feature Leakage in Collaborative Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).

[27]  Shiho Moriai,et al.  Privacy-Preserving Deep Learning via Additively Homomorphic Encryption , 2018, IEEE Transactions on Information Forensics and Security.

[28]  Lili Su,et al.  Securing Distributed Machine Learning in High Dimensions , 2018, ArXiv.

[29]  Tudor Dumitras,et al.  Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks , 2018, NeurIPS.

[30]  Dimitris S. Papailiopoulos,et al.  DRACO: Byzantine-resilient Distributed Training via Redundant Gradients , 2018, ICML.

[31]  Kannan Ramchandran,et al.  Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates , 2018, ICML.

[32]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[33]  Rachid Guerraoui,et al.  Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent , 2017, NIPS.

[34]  Richard Nock,et al.  Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption , 2017, ArXiv.

[35]  Sarvar Patel,et al.  Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..

[36]  H. Brendan McMahan,et al.  Learning Differentially Private Recurrent Language Models , 2017, ICLR.

[37]  Brendan Dolan-Gavitt,et al.  BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain , 2017, ArXiv.

[38]  Giuseppe Ateniese,et al.  Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning , 2017, CCS.

[39]  Vitaly Shmatikov,et al.  Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[40]  Ian Goodfellow,et al.  Deep Learning with Differential Privacy , 2016, CCS.

[41]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[42]  Somesh Jha,et al.  Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.

[43]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[44]  Blaine Nelson,et al.  Poisoning Attacks against Support Vector Machines , 2012, ICML.

[45]  Blaine Nelson,et al.  Support Vector Machines Under Adversarial Label Noise , 2011, ACML.

[46]  J. D. Tygar.  Adversarial Machine Learning , 2011, IEEE Internet Comput..

[47]  Blaine Nelson,et al.  Can machine learning be secure? , 2006, ASIACCS '06.

[48]  Chris Clifton,et al.  Privacy-preserving distributed mining of association rules on horizontally partitioned data , 2004, IEEE Transactions on Knowledge and Data Engineering.

[49]  Jaideep Vaidya,et al.  Privacy preserving association rule mining in vertically partitioned data , 2002, KDD.