Distributed Training papers, 2023–2026

Key findings

• Distributed Training went from 95 papers in 2023 to 98 in 2026, roughly 1.0x in three years.
• NeurIPS publishes the most Distributed Training work: 208 papers from 2023 to 2026, 53% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 1 in 2023 to 10 in 2026.
• Among the 5 topics under Efficiency and Systems, it ranks #4 by 2026 paper count; the fastest grower in the group, Hardware-Aware ML, is up 4.4x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026AgentWeave: Efficient Distributed Agent Serving via Flow Decomposition
• ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
• ICLR 2026Cascadia: An Efficient Cascade Serving System for Large Language Models
• CVPR 2026Finding Distributed Object-Centric Properties in Self-Supervised Transformers
• NeurIPS 2025A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments
• ICML 2025BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach
• ICLR 2025Addressing Label Shift in Distributed Learning via Entropy Regularization
• CVPR 2025Real-time High-fidelity Gaussian Human Avatars with Position-based Interpolation of Spatially Distributed MLPs
• NeurIPS 2024Communication Efficient Distributed Training with Distributed Lion
• ICML 2024Embarrassingly Parallel GFlowNets
• ICLR 2024Benchmarking Algorithms for Federated Domain Generalization
• CVPR 2024Deciphering 'What' and 'Where' Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations
• NeurIPS 20232Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression
• ICML 2023Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures
• ICLR 2023Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
• CVPR 2023DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training
• NeurIPS 2026Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods
• ICML 2026Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers
• ICLR 2026DualMap: Enabling Both Cache Affinity and Load Balancing for Distributed LLM Serving
• CVPR 2026Parallel Rigidity Matters for Bundle Adjustment

All Distributed Training papers

396 papers, page 5 of 8

201. NeurIPS 2025Progressive Data Dropout: An Embarrassingly Simple Approach to Train Faster
202. NeurIPS 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
203. NeurIPS 2025Quartet: Native FP4 Training Can Be Optimal for Large Language Models
204. NeurIPS 2025REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving
205. NeurIPS 2025Refinement Methods for Distributed Distribution Estimation under $\ell^p$-Losses
206. NeurIPS 2025Repo2Run: Automated Building Executable Environment for Code Repository at Scale
207. NeurIPS 2025Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means
208. NeurIPS 2025SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training
209. NeurIPS 2025Sketched Adaptive Distributed Deep Learning: A Sharp Convergence Analysis
210. NeurIPS 2025Skrull: Towards Efficient Long Context Fine-tuning through Dynamic Data Scheduling
211. NeurIPS 2025SmartCache: Context-aware Semantic Cache for Efficient Multi-turn LLM Inference
212. NeurIPS 2025SpaceServe: Spatial Multiplexing of Complementary Encoders and Decoders for Multimodal LLMs
213. NeurIPS 2025SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs
214. NeurIPS 2025SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning
215. NeurIPS 2025StarTrail: Concentric Ring Sequence Parallelism for Efficient Near-Infinite-Context Transformer Model Training
216. NeurIPS 2025Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism
217. NeurIPS 2025Synergistic Tensor and Pipeline Parallelism
218. NeurIPS 2025Tensor-Parallelism with Partially Synchronized Activations
219. NeurIPS 2025Tight analyses of first-order methods with error feedback
220. NeurIPS 2025Tight Bounds for Maximum Weight Matroid Independent Set and Matching in the Zero Communication Model
221. NeurIPS 2025Tight Bounds on the Distortion of Randomized and Deterministic Distributed Voting
222. NeurIPS 2025Towards Robust Parameter-Efficient Fine-Tuning for Federated Learning
223. NeurIPS 2025Transcending Cost-Quality Tradeoff in Agent Serving via Session-Awareness
224. NeurIPS 2025Understanding outer learning rates in Local SGD
225. NeurIPS 2025Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
226. NeurIPS 2025VLM in a flash: I/O-Efficient Sparsification of Vision-Language Model via Neuron Chunking
227. NeurIPS 2025ZeCO: Zero-Communication Overhead Sequence Parallelism for Linear Attention
228. CVPR 2024Deciphering 'What' and 'Where' Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations
229. CVPR 2024DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
230. CVPR 2024JointSQ: Joint Sparsification-Quantization for Distributed Learning
231. CVPR 2024Real-Time Neural BRDF with Spherically Distributed Primitives
232. CVPR 2024VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction
233. ICLR 2024A Quadratic Synchronization Rule for Distributed Deep Learning
234. ICLR 2024Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
235. ICLR 2024An improved analysis of per-sample and per-update clipping in federated learning
236. ICLR 2024Benchmarking Algorithms for Federated Domain Generalization
237. ICLR 2024Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
238. ICLR 2024CO2: Efficient Distributed Training with Full Communication-Computation Overlap
239. ICLR 2024Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
240. ICLR 2024EControl: Fast Distributed Optimization with Compression and Error Control
241. ICLR 2024Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants
242. ICLR 2024Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
243. ICLR 2024On the Effect of Batch Size in Byzantine-Robust Distributed Learning
244. ICLR 2024SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
245. ICML 2024Accelerating Federated Learning with Quick Distributed Mean Estimation
246. ICML 2024Distributed Bilevel Optimization with Communication Compression
247. ICML 2024Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery
248. ICML 2024Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
249. ICML 2024Embarrassingly Parallel GFlowNets
250. ICML 2024Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints

FAQ

How many Distributed Training papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

396 in total: 95 in 2023, 74 in 2024, 129 in 2025, 98 in 2026.

How many Distributed Training papers are at NeurIPS 2026?

NeurIPS 2026 has 36 Distributed Training papers, versus 69 the year before.

How many Distributed Training papers are at ICLR 2026?

ICLR 2026 has 18 Distributed Training papers, versus 18 the year before.

How many Distributed Training papers are at ICML 2026?

ICML 2026 has 42 Distributed Training papers, versus 40 the year before.

How many Distributed Training papers are at CVPR 2026?

CVPR 2026 has 2 Distributed Training papers, versus 2 the year before.

Which conference publishes the most Distributed Training papers?

NeurIPS, with 208 papers from 2023 to 2026.