On-Device Learning papers, 2023–2026

Key findings

• On-Device Learning fell from 122 papers in 2023 to 6 in 2026, so interest is cooling.
• NeurIPS publishes the most On-Device Learning work: 91 papers from 2023 to 2026, 68% of the four-venue total.
• Among the 5 topics under Edge and IoT, it ranks #4 by 2026 paper count; the fastest grower in the group, Sensor Fusion, is up 3.8x versus 0.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026MobileMoE: Scaling On-Device Mixture of Experts
• ICML 2026Lower Bounds for Frank-Wolfe on Strongly Convex Sets
• CVPR 2026DeDelayed Deleting Remote Inference Delay via On-Device Correction
• ICLR 2025Robust Function-Calling for On-Device Language Model via Function Masking
• CVPR 2025EdgeTAM: On-Device Track Anything Model
• ICML 2024MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
• CVPR 2024Device-Wise Federated Network Pruning
• NeurIPS 2023[Re] Numerical influence of ReLU'(0) on backpropagation
• ICML 2023Adversarial Collaborative Learning on Non-IID Features
• CVPR 2023Efficient On-Device Training via Gradient Filtering
• ICML 2026On the Fragility of Data Attribution When Learning Is Distributed
• CVPR 2026Mobile-VTON High-Fidelity On-Device Virtual Try-On
• ICML 2024On Online Experimentation without Device Identifiers
• CVPR 2024Cloud-Device Collaborative Learning for Multimodal Large Language Models
• NeurIPS 2023[Re] On the Reproducibility of CartoonX
• ICML 2023FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
• CVPR 2023TMO: Textured Mesh Acquisition of Objects With a Mobile Device by Using Differentiable Rendering
• ICML 2026Efficient Preference Poisoning Attack on Offline RLHF
• NeurIPS 2023A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
• ICML 2023Towards Robust Graph Incremental Learning on Evolving Graphs

All On-Device Learning papers

35 papers, page 1 of 1

1. ICML 2026Efficient Preference Poisoning Attack on Offline RLHF
2. ICML 2026Lower Bounds for Frank-Wolfe on Strongly Convex Sets
3. ICML 2026On the Fragility of Data Attribution When Learning Is Distributed
4. ICML 2024MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
5. ICML 2024On Online Experimentation without Device Identifiers
6. ICML 2023A Mathematical Model for Curriculum Learning for Parities
7. ICML 2023Adaptive Smoothing Gradient Learning for Spiking Neural Networks
8. ICML 2023Adversarial Collaborative Learning on Non-IID Features
9. ICML 2023An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
10. ICML 2023Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures
11. ICML 2023Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
12. ICML 2023Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
13. ICML 2023Correcting discount-factor mismatch in on-policy policy gradient methods
14. ICML 2023Cramming: Training a Language Model on a single GPU in one day.
15. ICML 2023Differentially Private Optimization on Large Model at Small Cost
16. ICML 2023Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation
17. ICML 2023FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
18. ICML 2023Global Selection of Contrastive Batches via Optimization on Sample Permutations
19. ICML 2023Learning Globally Smooth Functions on Manifolds
20. ICML 2023Learning Preconditioners for Conjugate Gradient PDE Solvers
21. ICML 2023Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
22. ICML 2023On Many-Actions Policy Gradient
23. ICML 2023On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization
24. ICML 2023On the Privacy-Robustness-Utility Trilemma in Distributed Learning
25. ICML 2023On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
26. ICML 2023PixelAsParam: A Gradient View on Diffusion Sampling with Guidance
27. ICML 2023Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
28. ICML 2023Provably Invariant Learning without Domain Information
29. ICML 2023Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
30. ICML 2023QASA: Advanced Question Answering on Scientific Articles
31. ICML 2023Rethinking Weak Supervision in Helping Contrastive Learning
32. ICML 2023StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
33. ICML 2023Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming
34. ICML 2023Towards Robust Graph Incremental Learning on Evolving Graphs
35. ICML 2023Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression

FAQ

How many On-Device Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

134 in total: 122 in 2023, 4 in 2024, 2 in 2025, 6 in 2026.

How many On-Device Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 1 On-Device Learning papers.

How many On-Device Learning papers are at ICLR 2025?

ICLR 2025 has 1 On-Device Learning papers.

How many On-Device Learning papers are at ICML 2026?

ICML 2026 has 3 On-Device Learning papers.

How many On-Device Learning papers are at CVPR 2026?

CVPR 2026 has 2 On-Device Learning papers, versus 1 the year before.

Which conference publishes the most On-Device Learning papers?

NeurIPS, with 91 papers from 2023 to 2026.