← Learning Paradigms
Machine Learning › Learning Paradigms ›

Federated Learning

551 directly classified papers

Papers per year

Papers

Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem CVPR 2020
A Federated Approach in Training Acoustic Models INTERSPEECH 2020
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms JMLR 2020
Assisted Learning: A Framework for Multi-Organization Learning NIPS 2020
Federated Bayesian Optimization via Thompson Sampling NIPS 2020
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning NIPS 2020
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization NIPS 2020
FedSplit: an algorithmic framework for fast federated optimization NIPS 2020
Federated Principal Component Analysis NIPS 2020
Two-stage Federated Phenotyping and Patient Representation Learning ACL 2019
Agnostic Federated Learning ICML 2019
Embarrassingly Parallel Inference for Gaussian Processes JMLR 2019
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems AAAI 2019
Nonparametric Bayesian Aggregation for Massive Data JMLR 2019
Distributed Primal-Dual Optimization for Non-uniformly Distributed Data IJCAI 2018
Effective Crowdsourcing for a New Type of Summarization Task NAACL 2018
Decentralized Collaborative Learning of Personalized Models over Networks AISTATS 2017
Collaborative PAC Learning NIPS 2017
Efficient Distributed Learning with Sparsity ICML 2017
Distributed Semi-supervised Learning with Kernel Ridge Regression JMLR 2017
Distributed Inference for Dirichlet Process Mixture Models ICML 2015
Linear and Parallel Learning of Markov Random Fields ICML 2014
Protocols for Learning Classifiers on Distributed Data AISTATS 2012
A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification AISTATS 2012
Distributed Probabilistic Learning for Camera Networks with Missing Data NIPS 2012