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 2 of 2

51. NeurIPS 2024FM-Delta: Lossless Compression for Storing Massive Fine-tuned Foundation Models
52. NeurIPS 2024Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products
53. NeurIPS 2024HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links
54. NeurIPS 2024Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
55. NeurIPS 2024Leveraging partial stragglers within gradient coding
56. NeurIPS 2024Loki: Low-rank Keys for Efficient Sparse Attention
57. NeurIPS 2024LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search
58. NeurIPS 2024LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
59. NeurIPS 2024Mini-Sequence Transformers: Optimizing Intermediate Memory for Long Sequences Training
60. NeurIPS 2024Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity
61. NeurIPS 2024Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime
62. NeurIPS 2024OxonFair: A Flexible Toolkit for Algorithmic Fairness
63. NeurIPS 2024pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning
64. NeurIPS 2024Pipeline Parallelism with Controllable Memory
65. NeurIPS 2024RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks
66. NeurIPS 2024Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models
67. NeurIPS 2024SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
68. NeurIPS 2024SGLang: Efficient Execution of Structured Language Model Programs
69. NeurIPS 2024Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity
70. NeurIPS 2024Sketching for Distributed Deep Learning: A Sharper Analysis
71. NeurIPS 2024SpeedLoader: An I/O efficient scheme for heterogeneous and distributed LLM operation
72. NeurIPS 2024Temporal-Difference Learning Using Distributed Error Signals
73. NeurIPS 2024TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices
74. 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.