Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints
暂无分享,去创建一个
[1] Rich Caruana,et al. Multitask Learning , 1997, Machine-mediated learning.
[2] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[3] Jean-Philippe Vert,et al. Clustered Multi-Task Learning: A Convex Formulation , 2008, NIPS.
[4] Hal Daumé,et al. Learning Task Grouping and Overlap in Multi-task Learning , 2012, ICML.
[5] Somesh Jha,et al. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.
[6] Peter Richtárik,et al. Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.
[7] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[8] Blaise Agüera y Arcas,et al. Federated Learning of Deep Networks using Model Averaging , 2016, ArXiv.
[9] Michael I. Jordan,et al. CoCoA: A General Framework for Communication-Efficient Distributed Optimization , 2016, J. Mach. Learn. Res..
[10] Ameet Talwalkar,et al. Federated Multi-Task Learning , 2017, NIPS.
[11] Giuseppe Ateniese,et al. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning , 2017, CCS.
[12] Sebastian Ruder,et al. An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.
[13] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[14] Sarvar Patel,et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..
[15] Anit Kumar Sahu,et al. On the Convergence of Federated Optimization in Heterogeneous Networks , 2018, ArXiv.
[16] Martin Jaggi,et al. Sparsified SGD with Memory , 2018, NeurIPS.
[17] Yue Zhao,et al. Federated Learning with Non-IID Data , 2018, ArXiv.
[18] Gaurav Kapoor,et al. Protection Against Reconstruction and Its Applications in Private Federated Learning , 2018, ArXiv.
[19] Vitaly Shmatikov,et al. Inference Attacks Against Collaborative Learning , 2018, ArXiv.
[20] Sebastian Caldas,et al. LEAF: A Benchmark for Federated Settings , 2018, ArXiv.
[21] William J. Dally,et al. Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training , 2017, ICLR.
[22] Sebastian Caldas,et al. Expanding the Reach of Federated Learning by Reducing Client Resource Requirements , 2018, ArXiv.
[23] Peng Jiang,et al. A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication , 2018, NeurIPS.
[24] Shenghuo Zhu,et al. Parallel Restarted SGD for Non-Convex Optimization with Faster Convergence and Less Communication , 2018, ArXiv.
[25] Úlfar Erlingsson,et al. The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets , 2018, ArXiv.
[26] Vitaly Shmatikov,et al. Exploiting Unintended Feature Leakage in Collaborative Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[27] Martin Jaggi,et al. Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication , 2019, ICML.
[28] Úlfar Erlingsson,et al. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks , 2018, USENIX Security Symposium.
[29] Klaus-Robert Müller,et al. Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication , 2018, 2019 International Joint Conference on Neural Networks (IJCNN).
[30] Qiang Yang,et al. Federated Machine Learning , 2019, ACM Trans. Intell. Syst. Technol..
[31] Hubert Eichner,et al. Towards Federated Learning at Scale: System Design , 2019, SysML.
[32] Kannan Ramchandran,et al. Robust Federated Learning in a Heterogeneous Environment , 2019, ArXiv.
[33] Joachim M. Buhmann,et al. Variational Federated Multi-Task Learning , 2019, ArXiv.
[34] Shenghuo Zhu,et al. Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning , 2018, AAAI.
[35] Klaus-Robert Müller,et al. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[36] Anit Kumar Sahu,et al. Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.
[37] Martin Jaggi,et al. Decentralized Deep Learning with Arbitrary Communication Compression , 2019, ICLR.
[38] Tao Lin,et al. Don't Use Large Mini-Batches, Use Local SGD , 2018, ICLR.
[39] Xiang Li,et al. On the Convergence of FedAvg on Non-IID Data , 2019, ICLR.
[40] Anit Kumar Sahu,et al. Federated Optimization in Heterogeneous Networks , 2018, MLSys.
[41] Heiko Schwarz,et al. DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks , 2019, IEEE Journal of Selected Topics in Signal Processing.
[42] Klaus-Robert Müller,et al. Compact and Computationally Efficient Representation of Deep Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.