Three Approaches for Personalization with Applications to Federated Learning
暂无分享,去创建一个
Y. Mansour | M. Mohri | A. Suresh | Jae Ro
[1] Vladimir Vapnik,et al. Principles of Risk Minimization for Learning Theory , 1991, NIPS.
[2] Inderjit S. Dhillon,et al. Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..
[3] Andrew Gelman,et al. Multilevel (Hierarchical) Modeling: What It Can and Cannot Do , 2006, Technometrics.
[4] Koby Crammer,et al. Learning Bounds for Domain Adaptation , 2007, NIPS.
[5] Yishay Mansour,et al. Domain Adaptation with Multiple Sources , 2008, NIPS.
[6] Anil K. Jain. Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..
[7] Yishay Mansour,et al. Domain Adaptation: Learning Bounds and Algorithms , 2009, COLT.
[8] Mehryar Mohri,et al. New Analysis and Algorithm for Learning with Drifting Distributions , 2012, ALT.
[9] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[10] Yoshua Bengio,et al. An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks , 2013, ICLR.
[11] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[12] Peter Richtárik,et al. Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.
[13] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[14] Ameet Talwalkar,et al. Federated Multi-Task Learning , 2017, NIPS.
[15] Dong Yu,et al. Recent progresses in deep learning based acoustic models , 2017, IEEE/CAA Journal of Automatica Sinica.
[16] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[17] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[18] Sarvar Patel,et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..
[19] Ananda Theertha Suresh,et al. Distributed Mean Estimation with Limited Communication , 2016, ICML.
[20] Walid Saad,et al. Federated Learning for Ultra-Reliable Low-Latency V2V Communications , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).
[21] Nathan Srebro,et al. Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization , 2018, NeurIPS.
[22] Zhenguo Li,et al. Federated Meta-Learning with Fast Convergence and Efficient Communication , 2018, 1802.07876.
[23] Yue Zhao,et al. Federated Learning with Non-IID Data , 2018, ArXiv.
[24] Wei Shi,et al. Federated learning of predictive models from federated Electronic Health Records , 2018, Int. J. Medical Informatics.
[25] Sebastian Caldas,et al. LEAF: A Benchmark for Federated Settings , 2018, ArXiv.
[26] Sanjiv Kumar,et al. cpSGD: Communication-efficient and differentially-private distributed SGD , 2018, NeurIPS.
[27] Hubert Eichner,et al. Federated Learning for Mobile Keyboard Prediction , 2018, ArXiv.
[28] Hubert Eichner,et al. APPLIED FEDERATED LEARNING: IMPROVING GOOGLE KEYBOARD QUERY SUGGESTIONS , 2018, ArXiv.
[29] José M. F. Moura,et al. Adversarial Multiple Source Domain Adaptation , 2018, NeurIPS.
[30] Sashank J. Reddi,et al. SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning , 2019, ArXiv.
[31] Mehryar Mohri,et al. Agnostic Federated Learning , 2019, ICML.
[32] Swaroop Ramaswamy,et al. Federated Learning for Emoji Prediction in a Mobile Keyboard , 2019, ArXiv.
[33] Hubert Eichner,et al. Federated Evaluation of On-device Personalization , 2019, ArXiv.
[34] Sunav Choudhary,et al. Federated Learning with Personalization Layers , 2019, ArXiv.
[35] Qiang Yang,et al. Federated Machine Learning , 2019, ACM Trans. Intell. Syst. Technol..
[36] Sebastian U. Stich,et al. Local SGD Converges Fast and Communicates Little , 2018, ICLR.
[37] Kang G. Shin,et al. Federated User Representation Learning , 2019, ArXiv.
[38] Cyril Allauzen,et al. Federated Learning of N-Gram Language Models , 2019, CoNLL.
[39] Virendra J. Marathe,et al. Private Federated Learning with Domain Adaptation , 2019, ArXiv.
[40] Maria-Florina Balcan,et al. Adaptive Gradient-Based Meta-Learning Methods , 2019, NeurIPS.
[41] Joachim M. Buhmann,et al. Variational Federated Multi-Task Learning , 2019, ArXiv.
[42] Sreeram Kannan,et al. Improving Federated Learning Personalization via Model Agnostic Meta Learning , 2019, ArXiv.
[43] Tom Ouyang,et al. Federated Learning Of Out-Of-Vocabulary Words , 2019, ArXiv.
[44] H. Brendan McMahan,et al. Generative Models for Effective ML on Private, Decentralized Datasets , 2019, ICLR.
[45] Vitaly Shmatikov,et al. Salvaging Federated Learning by Local Adaptation , 2020, ArXiv.
[46] Mehrdad Mahdavi,et al. Adaptive Personalized Federated Learning , 2020, ArXiv.
[47] Milind Kulkarni,et al. Survey of Personalization Techniques for Federated Learning , 2020, 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4).
[48] Anit Kumar Sahu,et al. Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.
[49] Chen-Yu Wei,et al. Federated Residual Learning , 2020, ArXiv.
[50] Peter Richtárik,et al. Federated Learning of a Mixture of Global and Local Models , 2020, ArXiv.
[51] Aryan Mokhtari,et al. Personalized Federated Learning: A Meta-Learning Approach , 2020, ArXiv.
[52] Sashank J. Reddi,et al. SCAFFOLD: Stochastic Controlled Averaging for Federated Learning , 2019, ICML.
[53] Ruslan Salakhutdinov,et al. Think Locally, Act Globally: Federated Learning with Local and Global Representations , 2020, ArXiv.
[54] Richard Nock,et al. Advances and Open Problems in Federated Learning , 2019, Found. Trends Mach. Learn..
[55] Wojciech Samek,et al. Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints , 2019, IEEE Transactions on Neural Networks and Learning Systems.