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Distributed Learning

1100 directly classified papers

Papers per year

Papers

Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data ICML 2021
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks ICML 2021
RelaySum for Decentralized Deep Learning on Heterogeneous Data NIPS 2021
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning NIPS 2021
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback NIPS 2021
Preserved central model for faster bidirectional compression in distributed settings NIPS 2021
Distributed Principal Component Analysis with Limited Communication NIPS 2021
Communication-efficient SGD: From Local SGD to One-Shot Averaging NIPS 2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices NIPS 2021
Newton Method over Networks is Fast up to the Statistical Precision ICML 2021
Extreme k-Center Clustering AAAI 2021
Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation AAAI 2021
DRIVE: One-bit Distributed Mean Estimation NIPS 2021
Distributed Covariance Steering with Consensus ADMM for Stochastic Multi-Agent Systems RSS 2021
A Stochastic Newton Algorithm for Distributed Convex Optimization NIPS 2021
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision IJCNLP 2021
Communication-Aware Collaborative Learning AAAI 2021
1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed ICML 2021
Distributed Deep Learning In Open Collaborations NIPS 2021
LocalNewton: Reducing communication rounds for distributed learning UAI 2021
Federated-EM with heterogeneity mitigation and variance reduction NIPS 2021
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem NIPS 2021
On Large-Cohort Training for Federated Learning NIPS 2021
Asynchronous Decentralized Online Learning NIPS 2021
Fault-Tolerant Replication with Pull-Based Consensus in MongoDB NSDI 2021