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

74 papers, page 1 of 2

1. CVPR 2024Deciphering 'What' and 'Where' Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations
2. CVPR 2024DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
3. CVPR 2024JointSQ: Joint Sparsification-Quantization for Distributed Learning
4. CVPR 2024Real-Time Neural BRDF with Spherically Distributed Primitives
5. CVPR 2024VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction
6. ICLR 2024A Quadratic Synchronization Rule for Distributed Deep Learning
7. ICLR 2024Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
8. ICLR 2024An improved analysis of per-sample and per-update clipping in federated learning
9. ICLR 2024Benchmarking Algorithms for Federated Domain Generalization
10. ICLR 2024Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
11. ICLR 2024CO2: Efficient Distributed Training with Full Communication-Computation Overlap
12. ICLR 2024Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
13. ICLR 2024EControl: Fast Distributed Optimization with Compression and Error Control
14. ICLR 2024Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants
15. ICLR 2024Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
16. ICLR 2024On the Effect of Batch Size in Byzantine-Robust Distributed Learning
17. ICLR 2024SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
18. ICML 2024Accelerating Federated Learning with Quick Distributed Mean Estimation
19. ICML 2024Distributed Bilevel Optimization with Communication Compression
20. ICML 2024Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery
21. ICML 2024Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
22. ICML 2024Embarrassingly Parallel GFlowNets
23. ICML 2024Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints
24. ICML 2024High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
25. ICML 2024LASER: Linear Compression in Wireless Distributed Optimization
26. NeurIPS 2024Accelerating Blockwise Parallel Language Models with Draft Refinement
27. NeurIPS 2024Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
28. NeurIPS 2024ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction
29. NeurIPS 2024B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
30. NeurIPS 2024Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
31. NeurIPS 2024Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
32. NeurIPS 2024Communication Bounds for the Distributed Experts Problem
33. NeurIPS 2024Communication Efficient Distributed Training with Distributed Lion
34. NeurIPS 2024Convergence Analysis of Split Federated Learning on Heterogeneous Data
35. NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
36. NeurIPS 2024Distributed Least Squares in Small Space via Sketching and Bias Reduction
37. NeurIPS 2024Distributed Sparse Regression via Penalization
38. NeurIPS 2024Distributed-Order Fractional Graph Operating Network
39. NeurIPS 2024DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus
40. NeurIPS 2024Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences
41. NeurIPS 2024Efficient Centroid-Linkage Clustering
42. NeurIPS 2024Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
43. NeurIPS 2024Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
44. NeurIPS 2024Efficient LLM Scheduling by Learning to Rank
45. NeurIPS 2024Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters
46. NeurIPS 2024Exponential Quantum Communication Advantage in Distributed Inference and Learning
47. NeurIPS 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
48. NeurIPS 2024Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level
49. NeurIPS 2024FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
50. NeurIPS 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

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.