distributed learning

EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback NeurIPS 2021 Effective Distributed Learning with Random Features: Improved Bounds and Algorithms ICLR 2021 Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent AAAI 2021 ErrorCompensatedX: error compensation for variance reduced algorithms NeurIPS 2021 Escaping Saddle Points with Compressed SGD NeurIPS 2021 FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection JMLR 2021 Federated-EM with heterogeneity mitigation and variance reduction NeurIPS 2021 Federated Learning Based on Dynamic Regularization ICLR 2021 Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms ICLR 2021 Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes AISTATS 2021 Learning from History for Byzantine Robust Optimization ICML 2021 Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation NeurIPS 2021 Linear Regression over Networks with Communication Guarantees L4DC 2021 LocalNewton: Reducing communication rounds for distributed learning UAI 2021 Local SGD: Unified Theory and New Efficient Methods AISTATS 2021 Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency AISTATS 2021 Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NeurIPS 2021 MARINA: Faster Non-Convex Distributed Learning with Compression ICML 2021 Model-Contrastive Federated Learning CVPR 2021 Model Linkage Selection for Cooperative Learning JMLR 2021 Near Optimal Distributed Learning of Halfspaces with Two Parties COLT 2021 One-Round Communication Efficient Distributed M-Estimation AISTATS 2021 On InstaHide, Phase Retrieval, and Sparse Matrix Factorization ICLR 2021 On Large-Cohort Training for Federated Learning NeurIPS 2021 Optimal Complexity in Decentralized Training ICML 2021