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

42 papers, page 1 of 1

1. ICML 2026A Short and Unified Convergence Analysis of the SAG, SAGA, and IAG Algorithms
2. ICML 2026A Tight Theory of Error Feedback Algorithms in Distributed Optimization
3. ICML 2026Accelerated Dual Method for Distributed Optimization: An Inexact-Gradient View of Local Updates
4. ICML 2026Adaptive Batch Sizes Using Non-Euclidean Gradient Noise Scales for Stochastic Sign and Spectral Descent
5. ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
6. ICML 2026Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers
7. ICML 2026CoCoQuant: Breaking the Bandwidth Wall via Co-Optimized Communication and Computation Quantization
8. ICML 2026CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
9. ICML 2026Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings
10. ICML 2026Decoupled Low-Rank Adaptation for Robust Federated Fine-Tuning
11. ICML 2026Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation
12. ICML 2026DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
13. ICML 2026Distributed Stochastic $K$-Level Optimization Over Networks
14. ICML 2026DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs
15. ICML 2026Dynamics of neural scaling laws in random feature regression with powerlaw-distributed kernel eigenvalues
16. ICML 2026EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference
17. ICML 2026Efficient Distributed MLLM Training with Cornstarch
18. ICML 2026FAB: A First-Order AB-based Gradient Algorithm for Distributed Bilevel Optimization over Time-Varying Directed Graphs
19. ICML 2026Factored Gossip DiLoCo: Reducing Blocking Communication within DiLoCo
20. ICML 2026FedARC: Anchor-Guided Residual Compensation for Data and Model Heterogeneous Federated Learning
21. ICML 2026FedCDWA: Decoupled Federated Prototype Distillation with Hierarchical Wasserstein Aggregation
22. ICML 2026FedSSM: State Space Model-based Proactive Inference for Heterogeneous Multimodal Federated Learning
23. ICML 2026Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control
24. ICML 2026gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
25. ICML 2026HexGen-3: A Fully Disaggregated LLM Serving Framework with Fine-Grained Heterogeneous Resource Autoscaling
26. ICML 2026High-Probability Convergence Guarantees of Decentralized SGD
27. ICML 2026Identifying and Mitigating Errors in Gradient Aggregation of Distributed Data Parallel Training
28. ICML 2026JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
29. ICML 2026Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts
30. ICML 2026LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
31. ICML 2026Matrix-Free GPU Semidefinite Programming for Quantum Ordered Search at the k=6 Frontier
32. ICML 2026Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation
33. ICML 2026MuLoCo: Muon is a Practical Inner Optimizer for DiLoCo
34. ICML 2026On the Role of Batch Size in Stochastic Conditional Gradient Methods
35. ICML 2026One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining
36. ICML 2026OServe: Accelerating LLM Serving via Spatial-Temporal Workload Orchestration
37. ICML 2026Random Scaling of Emergent Capabilities
38. ICML 2026Robust Federated Learning Against Adaptive Compression
39. ICML 2026Row-Stochastic Matrices Can Provably Outperform Doubly Stochastic Matrices in Decentralized Learning
40. ICML 2026scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics
41. ICML 2026SFedPO: Streaming Federated Learning with a Prediction Oracle under Temporal Shifts
42. ICML 2026Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning

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.