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

396 papers, page 3 of 8

101. ICLR 2025Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats
102. ICLR 2025Addressing Label Shift in Distributed Learning via Entropy Regularization
103. ICLR 2025An Asynchronous Bundle Method for Distributed Learning Problems
104. ICLR 2025Deep Distributed Optimization for Large-Scale Quadratic Programming
105. ICLR 2025Distributed Speculative Inference (DSI): Speculation Parallelism for Provably Faster Lossless Language Model Inference
106. ICLR 2025EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models
107. ICLR 2025From Promise to Practice: Realizing High-performance Decentralized Training
108. ICLR 2025Generalization and Distributed Learning of GFlowNets
109. ICLR 2025Learning Successor Features with Distributed Hebbian Temporal Memory
110. ICLR 2025Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression
111. ICLR 2025LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
112. ICLR 2025Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
113. ICLR 2025NetMoE: Accelerating MoE Training through Dynamic Sample Placement
114. ICLR 2025Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing
115. ICLR 2025PETRA: Parallel End-to-end Training with Reversible Architectures
116. ICLR 2025Preble: Efficient Distributed Prompt Scheduling for LLM Serving
117. ICLR 2025TorchTitan: One-stop PyTorch native solution for production ready LLM pretraining
118. ICLR 2025Uni-Sign: Toward Unified Sign Language Understanding at Scale
119. ICML 2025Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
120. ICML 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
121. ICML 2025ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
122. ICML 2025Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
123. ICML 2025BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach
124. ICML 2025Causality Inspired Federated Learning for OOD Generalization
125. ICML 2025Deep Streaming View Clustering
126. ICML 2025Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs
127. ICML 2025Distributed Conformal Prediction via Message Passing
128. ICML 2025Distributed Event-Based Learning via ADMM
129. ICML 2025Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal
130. ICML 2025Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning
131. ICML 2025Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold
132. ICML 2025DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
133. ICML 2025DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence
134. ICML 2025Efficient Distributed Optimization under Heavy-Tailed Noise
135. ICML 2025Efficiently Serving Large Multimodal Models Using EPD Disaggregation
136. ICML 2025Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
137. ICML 2025FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning
138. ICML 2025FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation
139. ICML 2025HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training
140. ICML 2025How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective
141. ICML 2025Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
142. ICML 2025Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
143. ICML 2025Layer-wise Quantization for Quantized Optimistic Dual Averaging
144. ICML 2025Learning curves theory for hierarchically compositional data with power-law distributed features
145. ICML 2025LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models
146. ICML 2025Nesterov Method for Asynchronous Pipeline Parallel Optimization
147. ICML 2025On Fine-Grained Distinct Element Estimation
148. ICML 2025On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning
149. ICML 2025Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead
150. ICML 2025Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds

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.