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Distributed Learning

1100 directly classified papers

Papers per year

Papers

zPROBE: Zero Peek Robustness Checks for Federated Learning ICCV 2023
A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization UAI 2023
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization ICCV 2023
Federated Learning for Commercial Image Sources WACV 2023
Federated learning of models pre-trained on different features with consensus graphs UAI 2023
DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training CVPR 2023
Robust Heterogeneous Federated Learning under Data Corruption ICCV 2023
Decentralized Learning With Multi-Headed Distillation CVPR 2023
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data ICML 2023
Fleet Active Learning: A Submodular Maximization Approach CORL 2023
Collaborative Learning via Prediction Consensus NIPS 2023
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost ICML 2023
Flor: An Open High Performance RDMA Framework Over Heterogeneous RNICs OSDI 2023
TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs NSDI 2023
Distributed Inference and Fine-tuning of Large Language Models Over The Internet NIPS 2023
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning NIPS 2023
FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation AAAI 2023
Petals: Collaborative Inference and Fine-tuning of Large Models ACL 2023
Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems NIPS 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis NIPS 2023
Parallel $Q$-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation ICML 2023
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs NSDI 2023
ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks NIPS 2023
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning ICML 2023
$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning NIPS 2023