Federated Learning

Differentially Private Contextual Linear Bandits NeurIPS 2018 Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization NeurIPS 2018 Do Outliers Ruin Collaboration? ICML 2018 LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning NeurIPS 2018 Learning Differentially Private Recurrent Language Models ICLR 2018 Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems AISTATS 2018 Personalized and Private Peer-to-Peer Machine Learning AISTATS 2018 The Hidden Vulnerability of Distributed Learning in Byzantium ICML 2018 Tight Bounds for Collaborative PAC Learning via Multiplicative Weights NeurIPS 2018 Asynchronous Distributed Variational Gaussian Process for Regression ICML 2017 Communication-Efficient Distributed Learning of Discrete Distributions NeurIPS 2017 Differentially private Bayesian learning on distributed data NeurIPS 2017 Federated Multi-Task Learning NeurIPS 2017 Lean Crowdsourcing: Combining Humans and Machines in an Online System CVPR 2017 Bootstrap Model Aggregation for Distributed Statistical Learning NeurIPS 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 Distributed Inference for Dirichlet Process Mixture Models ICML 2015 Fast Distributed k-Center Clustering with Outliers on Massive Data NeurIPS 2015 Streaming, Distributed Variational Inference for Bayesian Nonparametrics NeurIPS 2015 WASP: Scalable Bayes via barycenters of subset posteriors AISTATS 2015 Distributed Bayesian Posterior Sampling via Moment Sharing NeurIPS 2014 Distributed Parameter Estimation in Probabilistic Graphical Models NeurIPS 2014 Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem COLT 2013