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

18 papers, page 1 of 1

1. ICLR 2026A Scalable Distributed Framework for Multimodal GigaVoxel Image Registration
2. ICLR 2026Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
3. ICLR 2026Better Bounds for the Distributed Experts Problem
4. ICLR 2026Cascadia: An Efficient Cascade Serving System for Large Language Models
5. ICLR 2026DiSRouter: Distributed Self-Routing for LLM Selections
6. ICLR 2026Distributed Algorithms for Euclidean Clustering
7. ICLR 2026DualMap: Enabling Both Cache Affinity and Load Balancing for Distributed LLM Serving
8. ICLR 2026FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
9. ICLR 2026Ghost in the Cloud: Your Geo-Distributed Large Language Models Training is Easily Manipulated
10. ICLR 2026Heterogeneous Federated Fine-Tuning with Parallel One-Rank Adaptation
11. ICLR 2026LEGACY: A Lightweight Dynamic Gradient Compression Strategy for Distributed Deep Learning
12. ICLR 2026Long-Context Attention Benchmark: From Kernel Efficiency to Distributed Context Parallelism
13. ICLR 2026MT-DAO: Multi-Timescale Distributed Adaptive Optimizers with Local Updates
14. ICLR 2026Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters
15. ICLR 2026Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
16. ICLR 2026ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding
17. ICLR 2026Revisiting Parameter Server in LLM Post-Training
18. ICLR 2026Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data

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.