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

118 papers, page 2 of 3

51. ICML 2025Distributed Conformal Prediction via Message Passing
52. ICML 2025Distributed Event-Based Learning via ADMM
53. ICML 2025Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal
54. ICML 2025Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning
55. ICML 2025Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold
56. ICML 2025DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
57. ICML 2025DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence
58. ICML 2025Efficient Distributed Optimization under Heavy-Tailed Noise
59. ICML 2025Efficiently Serving Large Multimodal Models Using EPD Disaggregation
60. ICML 2025Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
61. ICML 2025FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning
62. ICML 2025FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation
63. ICML 2025HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training
64. ICML 2025How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective
65. ICML 2025Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
66. ICML 2025Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
67. ICML 2025Layer-wise Quantization for Quantized Optimistic Dual Averaging
68. ICML 2025Learning curves theory for hierarchically compositional data with power-law distributed features
69. ICML 2025LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models
70. ICML 2025Nesterov Method for Asynchronous Pipeline Parallel Optimization
71. ICML 2025On Fine-Grained Distinct Element Estimation
72. ICML 2025On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning
73. ICML 2025Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead
74. ICML 2025Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds
75. ICML 2025Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity
76. ICML 2025Safe-EF: Error Feedback for Non-smooth Constrained Optimization
77. ICML 2025Scalable First-order Method for Certifying Optimal k-Sparse GLMs
78. ICML 2025Shortcut-connected Expert Parallelism for Accelerating Mixture of Experts
79. ICML 2025SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models
80. ICML 2025The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
81. ICML 2025Unified Breakdown Analysis for Byzantine Robust Gossip
82. ICML 2025When, Where and Why to Average Weights?
83. ICML 2024Accelerating Federated Learning with Quick Distributed Mean Estimation
84. ICML 2024Distributed Bilevel Optimization with Communication Compression
85. ICML 2024Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery
86. ICML 2024Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
87. ICML 2024Embarrassingly Parallel GFlowNets
88. ICML 2024Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints
89. ICML 2024High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
90. ICML 2024LASER: Linear Compression in Wireless Distributed Optimization
91. ICML 2023Better Diffusion Models Further Improve Adversarial Training
92. ICML 2023Bi-directional Masks for Efficient N:M Sparse Training
93. ICML 2023Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures
94. ICML 2023DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
95. ICML 2023Differentially Private Distributed Bayesian Linear Regression with MCMC
96. ICML 2023Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
97. ICML 2023Distributed Linear Bandits under Communication Constraints
98. ICML 2023Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
99. ICML 2023DIVISION: Memory Efficient Training via Dual Activation Precision
100. ICML 2023EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression

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.