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

49 papers, page 1 of 1

1. NeurIPS 2024Accelerating Blockwise Parallel Language Models with Draft Refinement
2. NeurIPS 2024Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
3. NeurIPS 2024ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction
4. NeurIPS 2024B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
5. NeurIPS 2024Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift
6. NeurIPS 2024Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
7. NeurIPS 2024Communication Bounds for the Distributed Experts Problem
8. NeurIPS 2024Communication Efficient Distributed Training with Distributed Lion
9. NeurIPS 2024Convergence Analysis of Split Federated Learning on Heterogeneous Data
10. NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
11. NeurIPS 2024Distributed Least Squares in Small Space via Sketching and Bias Reduction
12. NeurIPS 2024Distributed Sparse Regression via Penalization
13. NeurIPS 2024Distributed-Order Fractional Graph Operating Network
14. NeurIPS 2024DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus
15. NeurIPS 2024Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences
16. NeurIPS 2024Efficient Centroid-Linkage Clustering
17. NeurIPS 2024Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
18. NeurIPS 2024Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
19. NeurIPS 2024Efficient LLM Scheduling by Learning to Rank
20. NeurIPS 2024Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters
21. NeurIPS 2024Exponential Quantum Communication Advantage in Distributed Inference and Learning
22. NeurIPS 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
23. NeurIPS 2024Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level
24. NeurIPS 2024FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
25. NeurIPS 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
26. NeurIPS 2024FM-Delta: Lossless Compression for Storing Massive Fine-tuned Foundation Models
27. NeurIPS 2024Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products
28. NeurIPS 2024HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links
29. NeurIPS 2024Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
30. NeurIPS 2024Leveraging partial stragglers within gradient coding
31. NeurIPS 2024Loki: Low-rank Keys for Efficient Sparse Attention
32. NeurIPS 2024LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search
33. NeurIPS 2024LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
34. NeurIPS 2024Mini-Sequence Transformers: Optimizing Intermediate Memory for Long Sequences Training
35. NeurIPS 2024Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity
36. NeurIPS 2024Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime
37. NeurIPS 2024OxonFair: A Flexible Toolkit for Algorithmic Fairness
38. NeurIPS 2024pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning
39. NeurIPS 2024Pipeline Parallelism with Controllable Memory
40. NeurIPS 2024RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks
41. NeurIPS 2024Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models
42. NeurIPS 2024SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
43. NeurIPS 2024SGLang: Efficient Execution of Structured Language Model Programs
44. NeurIPS 2024Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity
45. NeurIPS 2024Sketching for Distributed Deep Learning: A Sharper Analysis
46. NeurIPS 2024SpeedLoader: An I/O efficient scheme for heterogeneous and distributed LLM operation
47. NeurIPS 2024Temporal-Difference Learning Using Distributed Error Signals
48. NeurIPS 2024TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices
49. NeurIPS 2024Toward Efficient Inference for Mixture of Experts

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.