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 7 of 8

301. NeurIPS 2024Toward Efficient Inference for Mixture of Experts
302. CVPR 2023Compacting Binary Neural Networks by Sparse Kernel Selection
303. CVPR 2023DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training
304. CVPR 2023Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning
305. ICLR 2023Asynchronous Distributed Bilevel Optimization
306. ICLR 2023DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity
307. ICLR 2023Distributed Differential Privacy in Multi-Armed Bandits
308. ICLR 2023Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
309. ICLR 2023Does Deep Learning Learn to Abstract? A Systematic Probing Framework
310. ICLR 2023LDMIC: Learning-based Distributed Multi-view Image Coding
311. ICLR 2023Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment
312. ICLR 2023Sparse Distributed Memory is a Continual Learner
313. ICLR 2023The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium
314. ICLR 2023Why (and When) does Local SGD Generalize Better than SGD?
315. ICML 2023Better Diffusion Models Further Improve Adversarial Training
316. ICML 2023Bi-directional Masks for Efficient N:M Sparse Training
317. ICML 2023Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures
318. ICML 2023DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
319. ICML 2023Differentially Private Distributed Bayesian Linear Regression with MCMC
320. ICML 2023Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
321. ICML 2023Distributed Linear Bandits under Communication Constraints
322. ICML 2023Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
323. ICML 2023DIVISION: Memory Efficient Training via Dual Activation Precision
324. ICML 2023EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression
325. ICML 2023Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule
326. ICML 2023Fast Algorithms for Distributed k-Clustering with Outliers
327. ICML 2023Federated Conformal Predictors for Distributed Uncertainty Quantification
328. ICML 2023FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
329. ICML 2023GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
330. ICML 2023Learn to Accumulate Evidence from All Training Samples: Theory and Practice
331. ICML 2023Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit
332. ICML 2023Multi-Objective Population Based Training
333. ICML 2023Optimal Shrinkage for Distributed Second-Order Optimization
334. ICML 2023Parallel Neurosymbolic Integration with Concordia
335. ICML 2023Project and Forget: Solving Large-Scale Metric Constrained Problems
336. ICML 2023Quantized Distributed Training of Large Models with Convergence Guarantees
337. ICML 2023SGD with Large Step Sizes Learns Sparse Features
338. ICML 2023Spatial Implicit Neural Representations for Global-Scale Species Mapping
339. ICML 2023Stabilizing GANs' Training with Brownian Motion Controller
340. ICML 2023Unit Scaling: Out-of-the-Box Low-Precision Training
341. ICML 2023When does Privileged information Explain Away Label Noise?
342. ICML 2023Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition
343. NeurIPS 20232Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression
344. NeurIPS 2023A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting
345. NeurIPS 2023A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
346. NeurIPS 2023A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design
347. NeurIPS 2023AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
348. NeurIPS 2023Adversarial Training from Mean Field Perspective
349. NeurIPS 2023Adversarially Robust Distributed Count Tracking via Partial Differential Privacy
350. NeurIPS 2023American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers

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.