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Federated Learning

1,360 papers

Papers per year

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Papers

Communication Compression for Decentralized Training NIPS 2018
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization NIPS 2018
Differentially Private Contextual Linear Bandits NIPS 2018
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights NIPS 2018
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning NIPS 2018
Personalized and Private Peer-to-Peer Machine Learning AISTATS 2018
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems AISTATS 2018
The Hidden Vulnerability of Distributed Learning in Byzantium ICML 2018
Do Outliers Ruin Collaboration? ICML 2018
Federated Multi-Task Learning NIPS 2017
Communication-Efficient Distributed Learning of Discrete Distributions NIPS 2017
Differentially private Bayesian learning on distributed data NIPS 2017
Lean Crowdsourcing: Combining Humans and Machines in an Online System CVPR 2017
Asynchronous Distributed Variational Gaussian Process for Regression ICML 2017
Bootstrap Model Aggregation for Distributed Statistical Learning NIPS 2016
Distributed Multi-Task Learning AISTATS 2016
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters CVPR 2016
Semi-Supervised and Cross-Lingual Knowledge Transfer Learnings for DNN Hybrid Acoustic Models Under Low-Resource Conditions INTERSPEECH 2016
Streaming, Distributed Variational Inference for Bayesian Nonparametrics NIPS 2015
Fast Distributed k-Center Clustering with Outliers on Massive Data NIPS 2015
WASP: Scalable Bayes via barycenters of subset posteriors AISTATS 2015
Distributed Inference for Dirichlet Process Mixture Models ICML 2015
Distributed Bayesian Posterior Sampling via Moment Sharing NIPS 2014
Distributed Parameter Estimation in Probabilistic Graphical Models NIPS 2014
Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem COLT 2013