暂无分享,去创建一个
Tian Li | Virginia Smith | Ahmad Beirami | Shengyuan Hu | Virginia Smith | Tian Li | Shengyuan Hu | A. Beirami | Ahmad Beirami
[1] Fei Chen,et al. Federated Meta-Learning with Fast Convergence and Efficient Communication , 2018 .
[2] Yishay Mansour,et al. Three Approaches for Personalization with Applications to Federated Learning , 2020, ArXiv.
[3] Dawn Xiaodong Song,et al. Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning , 2017, ArXiv.
[4] Gregory Cohen,et al. EMNIST: an extension of MNIST to handwritten letters , 2017, CVPR 2017.
[5] Maria-Florina Balcan,et al. Adaptive Gradient-Based Meta-Learning Methods , 2019, NeurIPS.
[6] Yaoliang Yu,et al. FedMGDA+: Federated Learning meets Multi-objective Optimization , 2020, ArXiv.
[7] Ameet Talwalkar,et al. Federated Multi-Task Learning , 2017, NIPS.
[8] Filip Hanzely,et al. Federated Learning of a Mixture of Global and Local Models , 2020, ArXiv.
[9] Blaine Nelson,et al. Poisoning Attacks against Support Vector Machines , 2012, ICML.
[10] Razvan Pascanu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[11] Qiang Wang,et al. Data Poisoning Attacks on Federated Machine Learning , 2020, IEEE Internet of Things Journal.
[12] Minghong Fang,et al. Local Model Poisoning Attacks to Byzantine-Robust Federated Learning , 2019, USENIX Security Symposium.
[13] Percy Liang,et al. Fairness Without Demographics in Repeated Loss Minimization , 2018, ICML.
[14] Tudor Dumitras,et al. Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks , 2018, NeurIPS.
[15] Wen-Chuan Lee,et al. Trojaning Attack on Neural Networks , 2018, NDSS.
[16] Chen-Yu Wei,et al. Federated Residual Learning , 2020, ArXiv.
[17] Hubert Eichner,et al. Federated Evaluation of On-device Personalization , 2019, ArXiv.
[18] Sebastian Caldas,et al. LEAF: A Benchmark for Federated Settings , 2018, ArXiv.
[19] Nguyen H. Tran,et al. Personalized Federated Learning with Moreau Envelopes , 2020, NeurIPS.
[20] Antonio Robles-Kelly,et al. Hierarchically Fair Federated Learning , 2020, ArXiv.
[21] Mehryar Mohri,et al. Agnostic Federated Learning , 2019, ICML.
[22] Behrouz Touri,et al. Global Games With Noisy Information Sharing , 2015, IEEE Transactions on Signal and Information Processing over Networks.
[23] Samet Oymak,et al. Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks , 2019, AISTATS.
[24] Zaïd Harchaoui,et al. Robust Aggregation for Federated Learning , 2019, IEEE Transactions on Signal Processing.
[25] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[26] Kartik Sreenivasan,et al. Attack of the Tails: Yes, You Really Can Backdoor Federated Learning , 2020, NeurIPS.
[27] Kannan Ramchandran,et al. Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates , 2018, ICML.
[28] Rachid Guerraoui,et al. Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent , 2017, NIPS.
[29] Ananda Theertha Suresh,et al. Can You Really Backdoor Federated Learning? , 2019, ArXiv.
[30] Walter J. Scheirer,et al. Backdooring Convolutional Neural Networks via Targeted Weight Perturbations , 2018, 2020 IEEE International Joint Conference on Biometrics (IJCB).
[31] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[32] Brendan Dolan-Gavitt,et al. BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain , 2017, ArXiv.
[33] Tian Li,et al. Fair Resource Allocation in Federated Learning , 2019, ICLR.
[34] Mehrdad Mahdavi,et al. Adaptive Personalized Federated Learning , 2020, ArXiv.
[35] Jonas Geiping,et al. MetaPoison: Practical General-purpose Clean-label Data Poisoning , 2020, NeurIPS.
[36] Bo Li,et al. DBA: Distributed Backdoor Attacks against Federated Learning , 2020, ICLR.
[37] Vitaly Shmatikov,et al. How To Backdoor Federated Learning , 2018, AISTATS.
[38] Prateek Mittal,et al. Analyzing Federated Learning through an Adversarial Lens , 2018, ICML.
[39] Virginia Smith,et al. Tilted Empirical Risk Minimization , 2020, ICLR.
[40] Aryan Mokhtari,et al. Personalized Federated Learning: A Meta-Learning Approach , 2020, ArXiv.
[41] Ruslan Salakhutdinov,et al. Think Locally, Act Globally: Federated Learning with Local and Global Representations , 2020, ArXiv.
[42] Yu Hen Hu,et al. Vehicle classification in distributed sensor networks , 2004, J. Parallel Distributed Comput..
[43] Sreeram Kannan,et al. Improving Federated Learning Personalization via Model Agnostic Meta Learning , 2019, ArXiv.
[44] Massimiliano Pontil,et al. Regularized multi--task learning , 2004, KDD.
[45] Blaine Nelson,et al. Support Vector Machines Under Adversarial Label Noise , 2011, ACML.
[46] Vitaly Shmatikov,et al. Salvaging Federated Learning by Local Adaptation , 2020, ArXiv.