联邦边缘推理论文趋势 2023–2026

主要结论

• 联邦边缘推理论文从 2023 年的 43 篇增长到 2026 年的 45 篇，三年间扩大到约 1.0 倍。
• ICML 是发联邦边缘推理论文最多的会议，2023–2026 年共 60 篇，占四个会议总量的 33%。
• 交叉方向里涨得最快的是端侧推理：同时属于两个主题的论文从 2023 年的 3 篇增至 2026 年的 15 篇。
• 在「边缘与物联网」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的传感融合是 3.8 倍，联邦边缘推理是 1.0 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部联邦边缘推理论文

共 180 篇，第 1 / 4 页

1. CVPR 2026E2-SCI Elastic Edge-Cloud Speculative Decoding via Credit Inertia
2. CVPR 2026Edge-Focused Super-Resolution for Omnidirectional Images with Spherical Geometric Augmentation
3. CVPR 2026From Selection to Scheduling Federated Geometry-Aware Correction Makes Exemplar Replay
4. CVPR 2026Single-Round Scalable Analytic Federated Learning
5. CVPR 2026Submodel Extraction for Efficient and Personalized Federated Learning via Optimal
6. ICLR 2026Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
7. ICLR 2026DeepAFL: Deep Analytic Federated Learning
8. ICLR 2026Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach
9. ICLR 2026Fed-Duet: Dual Expert-Orchestrated Framework for Continual Federated Vision-Language Learning
10. ICLR 2026Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus
11. ICLR 2026Near Optimal Robust Federated Learning Against Data Poisoning Attack
12. ICLR 2026Robust Federated Inference
13. ICML 2026Benchmarking at the Edge of Comprehension
14. ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
15. ICML 2026Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
16. ICML 2026Credibility-Aware Weighting Federated Causal Discovery for Time Series
17. ICML 2026Edge-colored Clustering in Hypergraphs: A MaxECC Approximation
18. ICML 2026FedEMoE: Improving Personalization on Heterogeneous Federated Learning via Elastic Mixture of Experts Architecture
19. ICML 2026FedFit: Federated Dynamic Sparse Training via Fisher Information scoring
20. ICML 2026FedGain: Toward Negative-Gain-Free Client Collaboration in Federated Learning
21. ICML 2026FedReLa: Imbalanced Federated Learning via Re-Labeling
22. ICML 2026Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
23. ICML 2026Lightweight Federated Incremental Learning via Decoupled Replay
24. ICML 2026Momentum Further Constrains Sharpness at the Edge of Stochastic Stability
25. ICML 2026No Retraining at Edge: Efficient Resource-Aware Mixed-Precision Quantization via Federated Supernet Learning
26. ICML 2026Non-Euclidean Gradient Descent Operates at the Edge of Stability
27. ICML 2026PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
28. ICML 2026RADE: Random Add-Drop Edge as a Regularizer
29. ICML 2026ShapCCS: Shapley-Driven Client Coreset Selection in Federated Learning
30. ICML 2026Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
31. NeurIPS 2026A typed tensor language for federated learning
32. NeurIPS 2026ASAP: Fast Adaptive Sliding Agnostic Poisoning Attack on Federated Learning
33. NeurIPS 2026Does Sparse Connectivity Improve Generalization? Convolutional Networks Below the Edge of Stability
34. NeurIPS 2026Edge of Stability Selectively Shapes Learning Across the Data Distribution
35. NeurIPS 2026Edge of Stochastic Stability: Revisiting the Edge of Stability for SGD
36. NeurIPS 2026FedSEM: Mitigating Cross-Client Evidence Drift in Federated Multiple Instance Learning
37. NeurIPS 2026FedSOUL: Federated Continual Unlearning via Spectral Orthogonality
38. NeurIPS 2026Generalization at the Edge of Stability
39. NeurIPS 2026Geometry-Constrained Kolmogorov–Arnold Networks: Learning Edge Geometry via Banach Duality
40. NeurIPS 2026Geometry-Regularized Collapse Resistance via Consensus Enhancement for Federated Learning
41. NeurIPS 2026MainFL: Intertemporal Data Valuation for Robust Auction-based Federated Learning
42. NeurIPS 2026Rehearsal-Free Statistical Prototype Regularization for Federated Incremental Learning
43. NeurIPS 2026SGD at the Edge of Stability: The Stochastic Sharpness Gap
44. NeurIPS 2026ShadowFPT: Backdooring Federated Prompt Tuning via Shadow Triggers
45. NeurIPS 2026Stability-Enhanced Federated Learning with Accelerated Gradient
46. CVPR 2025AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
47. CVPR 2025Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
48. CVPR 2025FedCS: Coreset Selection for Federated Learning
49. CVPR 2025NoT: Federated Unlearning via Weight Negation
50. CVPR 2025Population Normalization for Federated Learning

常见问题

2023–2026 年四大会议一共有多少篇联邦边缘推理论文？

共 180 篇，按年份分别是 2023 年 43 篇、2024 年 47 篇、2025 年 45 篇、2026 年 45 篇。

NeurIPS 2026 有多少篇联邦边缘推理论文？

NeurIPS 2026 有 15 篇联邦边缘推理论文，上一届是 20 篇。

ICLR 2026 有多少篇联邦边缘推理论文？

ICLR 2026 有 7 篇联邦边缘推理论文，上一届是 10 篇。

ICML 2026 有多少篇联邦边缘推理论文？

ICML 2026 有 18 篇联邦边缘推理论文，上一届是 10 篇。

CVPR 2026 有多少篇联邦边缘推理论文？

CVPR 2026 有 5 篇联邦边缘推理论文，上一届是 5 篇。

哪个会议的联邦边缘推理论文最多？

ICML，2023–2026 年共 60 篇。