Federated Edge Inference papers, 2023–2026

Key findings

• Federated Edge Inference went from 43 papers in 2023 to 45 in 2026, roughly 1.0x in three years.
• ICML publishes the most Federated Edge Inference work: 60 papers from 2023 to 2026, 33% of the four-venue total.
• The fastest-growing overlap is with Edge Inference: papers tagged with both rose from 3 in 2023 to 15 in 2026.
• Among the 5 topics under Edge and IoT, it ranks #2 by 2026 paper count; the fastest grower in the group, Sensor Fusion, is up 3.8x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Does Sparse Connectivity Improve Generalization? Convolutional Networks Below the Edge of Stability
• ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
• ICLR 2026Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
• CVPR 2026E2-SCI Elastic Edge-Cloud Speculative Decoding via Credit Inertia
• NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
• ICML 2025Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
• ICLR 2025Connecting Federated ADMM to Bayes
• CVPR 2025Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
• NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
• ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
• ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
• CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
• NeurIPS 2023Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
• ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
• ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
• CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
• NeurIPS 2026Edge of Stability Selectively Shapes Learning Across the Data Distribution
• ICML 2026Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
• ICLR 2026DeepAFL: Deep Analytic Federated Learning
• CVPR 2026Edge-Focused Super-Resolution for Omnidirectional Images with Spherical Geometric Augmentation

All Federated Edge Inference papers

47 papers, page 1 of 1

1. CVPR 20243D Neural Edge Reconstruction
2. CVPR 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
3. CVPR 2024Communication-Efficient Federated Learning with Accelerated Client Gradient
4. CVPR 2024Device-Wise Federated Network Pruning
5. CVPR 2024DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
6. CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
7. CVPR 2024FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
8. CVPR 2024Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
9. CVPR 2024Relaxed Contrastive Learning for Federated Learning
10. CVPR 2024Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space
11. CVPR 2024Towards Efficient Replay in Federated Incremental Learning
12. CVPR 2024Traceable Federated Continual Learning
13. ICLR 2024Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
14. ICLR 2024Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
15. ICLR 2024Federated Wasserstein Distance
16. ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
17. ICLR 2024Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
18. ICLR 2024PeFLL: Personalized Federated Learning by Learning to Learn
19. ICLR 2024Robust Training of Federated Models with Extremely Label Deficiency
20. ICLR 2024VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
21. ICML 2024Accelerating Federated Learning with Quick Distributed Mean Estimation
22. ICML 2024Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
23. ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
24. ICML 2024Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning
25. ICML 2024Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization
26. ICML 2024Clustered Federated Learning via Gradient-based Partitioning
27. ICML 2024Decomposable Submodular Maximization in Federated Setting
28. ICML 2024Fair Federated Learning via the Proportional Veto Core
29. ICML 2024FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
30. ICML 2024Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
31. ICML 2024Federated Neuro-Symbolic Learning
32. ICML 2024Federated Representation Learning in the Under-Parameterized Regime
33. ICML 2024Federated Self-Explaining GNNs with Anti-shortcut Augmentations
34. ICML 2024FedMBridge: Bridgeable Multimodal Federated Learning
35. ICML 2024FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
36. ICML 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
37. ICML 2024Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
38. ICML 2024Recovering Labels from Local Updates in Federated Learning
39. ICML 2024Rethinking the Flat Minima Searching in Federated Learning
40. NeurIPS 2024A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
41. NeurIPS 2024Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
42. NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
43. NeurIPS 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
44. NeurIPS 2024Federated Learning over Connected Modes
45. NeurIPS 2024Low Precision Local Training is Enough for Federated Learning
46. NeurIPS 2024The Sample-Communication Complexity Trade-off in Federated Q-Learning
47. NeurIPS 2024Thinking Forward: Memory-Efficient Federated Finetuning of Language Models

FAQ

How many Federated Edge Inference papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

180 in total: 43 in 2023, 47 in 2024, 45 in 2025, 45 in 2026.

How many Federated Edge Inference papers are at NeurIPS 2026?

NeurIPS 2026 has 15 Federated Edge Inference papers, versus 20 the year before.

How many Federated Edge Inference papers are at ICLR 2026?

ICLR 2026 has 7 Federated Edge Inference papers, versus 10 the year before.

How many Federated Edge Inference papers are at ICML 2026?

ICML 2026 has 18 Federated Edge Inference papers, versus 10 the year before.

How many Federated Edge Inference papers are at CVPR 2026?

CVPR 2026 has 5 Federated Edge Inference papers, versus 5 the year before.

Which conference publishes the most Federated Edge Inference papers?

ICML, with 60 papers from 2023 to 2026.