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Federated Learning

1355 directly classified papers

Papers per year

Papers

RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets AAAI 2019
Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing JMLR 2019
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems AAAI 2019
Semi-Cyclic Stochastic Gradient Descent ICML 2019
Data Poisoning Attacks on Stochastic Bandits ICML 2019
Sketching for Latent Dirichlet-Categorical Models AISTATS 2019
Differentially Private Distributed Data Summarization under Covariate Shift NIPS 2019
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients NIPS 2019
Adaptive Gradient-Based Meta-Learning Methods NIPS 2019
A Little Is Enough: Circumventing Defenses For Distributed Learning NIPS 2019
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up NIPS 2019
Language Discrimination and Transfer Learning for Similar Languages: Experiments with Feature Combinations and Adaptation NAACL 2019
Guided Meta-Policy Search NIPS 2019
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback NIPS 2019
Sublinear Space Private Algorithms Under the Sliding Window Model ICML 2019
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning ICML 2019
Analyzing Federated Learning through an Adversarial Lens ICML 2019
Learning Auctions with Robust Incentive Guarantees NIPS 2019
Differentially Private Contextual Linear Bandits NIPS 2018
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights NIPS 2018
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization NIPS 2018
Communication Compression for Decentralized Training NIPS 2018
COLA: Decentralized Linear Learning NIPS 2018
Personalized and Private Peer-to-Peer Machine Learning AISTATS 2018
Do Outliers Ruin Collaboration? ICML 2018