parameter server

Di-PS: System-Algorithm Co-Design for Asynchronous and Heterogeneous Cross-cluster LLM Training at Scale NSDI 2026 HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training ICML 2025 Optimal Distributed Training With Co-Adaptive Data Parallelism in Heterogeneous Environments IJCAI 2025 Accelerating Neural Recommendation Training with Embedding Scheduling NSDI 2024 Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization ICLR 2024 Leveraging partial stragglers within gradient coding NeurIPS 2024 THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression NSDI 2024 GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models NeurIPS 2022 A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters OSDI 2020 Distributed Machine Learning through Heterogeneous Edge Systems AAAI 2020 Learning Efficient Parameter Server Synchronization Policies for Distributed SGD ICLR 2020 Distributed Learning over Unreliable Networks ICML 2019 Learning to Learn Gradient Aggregation by Gradient Descent IJCAI 2019 Scalable Distributed DL Training: Batching Communication and Computation AAAI 2019 Accumulated Gradient Normalization ACML 2017 Asynchronous Distributed Variational Gaussian Process for Regression ICML 2017 Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study IJCAI 2017 Deep learning with Elastic Averaging SGD NeurIPS 2015 Communication Efficient Distributed Machine Learning with the Parameter Server NeurIPS 2014 More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server NeurIPS 2013